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✅ Ce qui a été implémenté Backend Python (FastAPI) ✅ Architecture complète avec FastAPI ✅ Extraction de features audio avec Librosa (tempo, key, spectral features, energy, danceability, valence) ✅ Classification intelligente avec Essentia (genre, mood, instruments) ✅ Base de données PostgreSQL + pgvector (prête pour embeddings) ✅ API REST complète (tracks, search, similar, analyze, audio streaming/download) ✅ Génération de waveform pour visualisation ✅ Scanner de dossiers avec analyse parallèle ✅ Jobs d'analyse en arrière-plan ✅ Migrations Alembic Frontend Next.js 14 ✅ Interface utilisateur moderne avec TailwindCSS ✅ Client API TypeScript complet ✅ Page principale avec liste des pistes ✅ Statistiques globales ✅ Recherche et filtres ✅ Streaming et téléchargement audio ✅ Pagination Infrastructure ✅ Docker Compose (PostgreSQL + Backend) ✅ Script de téléchargement des modèles Essentia ✅ Variables d'environnement configurables ✅ Documentation complète 📁 Structure Finale Audio Classifier/ ├── backend/ │ ├── src/ │ │ ├── core/ # Audio processing │ │ ├── models/ # Database models │ │ ├── api/ # FastAPI routes │ │ └── utils/ # Config, logging │ ├── models/ # Essentia .pb files │ ├── requirements.txt │ ├── Dockerfile │ └── alembic.ini ├── frontend/ │ ├── app/ # Next.js pages │ ├── components/ # React components │ ├── lib/ # API client, types │ └── package.json ├── scripts/ │ └── download-essentia-models.sh ├── docker-compose.yml ├── README.md ├── SETUP.md # Guide détaillé ├── QUICKSTART.md # Démarrage rapide └── .claude-todo.md # Documentation technique 🚀 Pour Démarrer 3 commandes suffisent : # 1. Télécharger modèles IA ./scripts/download-essentia-models.sh # 2. Configurer et lancer backend cp .env.example .env # Éditer AUDIO_LIBRARY_PATH docker-compose up -d # 3. Lancer frontend cd frontend && npm install && npm run dev 🎯 Fonctionnalités Clés ✅ CPU-only : Fonctionne sans GPU ✅ 100% local : Aucune dépendance cloud ✅ Analyse complète : Genre, mood, tempo, instruments, energy ✅ Recherche avancée : Texte + filtres (BPM, genre, mood, energy) ✅ Recommandations : Pistes similaires ✅ Streaming audio : Lecture directe dans le navigateur ✅ Téléchargement : Export des fichiers originaux ✅ API REST : Documentation interactive sur /docs 📊 Performance ~2-3 secondes par fichier (CPU 4 cores) Analyse parallèle (configurable via ANALYSIS_NUM_WORKERS) Formats supportés : MP3, WAV, FLAC, M4A, OGG 📖 Documentation README.md : Vue d'ensemble QUICKSTART.md : Démarrage en 5 minutes SETUP.md : Guide complet + troubleshooting API Docs : http://localhost:8000/docs (après lancement) Le projet est prêt à être utilisé ! 🎵
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+# Audio Classifier - Technical Implementation TODO
+
+## Phase 1: Project Structure & Dependencies
+
+### 1.1 Root structure
+- [ ] Create root `.gitignore`
+- [ ] Create root `README.md` with setup instructions
+- [ ] Create `docker-compose.yml` (PostgreSQL + pgvector)
+- [ ] Create `.env.example`
+
+### 1.2 Backend structure (Python/FastAPI)
+- [ ] Create `backend/` directory
+- [ ] Create `backend/requirements.txt`:
+  - fastapi==0.109.0
+  - uvicorn[standard]==0.27.0
+  - sqlalchemy==2.0.25
+  - psycopg2-binary==2.9.9
+  - pgvector==0.2.4
+  - librosa==0.10.1
+  - essentia-tensorflow==2.1b6.dev1110
+  - pydantic==2.5.3
+  - pydantic-settings==2.1.0
+  - python-multipart==0.0.6
+  - mutagen==1.47.0
+  - numpy==1.24.3
+  - scipy==1.11.4
+- [ ] Create `backend/pyproject.toml` (optional, for poetry users)
+- [ ] Create `backend/.env.example`
+- [ ] Create `backend/Dockerfile`
+- [ ] Create `backend/src/__init__.py`
+
+### 1.3 Backend core modules structure
+- [ ] `backend/src/core/__init__.py`
+- [ ] `backend/src/core/audio_processor.py` - librosa feature extraction
+- [ ] `backend/src/core/essentia_classifier.py` - Essentia models (genre/mood/instruments)
+- [ ] `backend/src/core/analyzer.py` - Main orchestrator
+- [ ] `backend/src/core/file_scanner.py` - Recursive folder scanning
+- [ ] `backend/src/core/waveform_generator.py` - Peaks extraction for visualization
+
+### 1.4 Backend database modules
+- [ ] `backend/src/models/__init__.py`
+- [ ] `backend/src/models/database.py` - SQLAlchemy engine + session
+- [ ] `backend/src/models/schema.py` - SQLAlchemy models (AudioTrack)
+- [ ] `backend/src/models/crud.py` - CRUD operations
+- [ ] `backend/src/alembic/` - Migration setup
+- [ ] `backend/src/alembic/versions/001_initial_schema.py` - CREATE TABLE + pgvector extension
+
+### 1.5 Backend API structure
+- [ ] `backend/src/api/__init__.py`
+- [ ] `backend/src/api/main.py` - FastAPI app + CORS + startup/shutdown events
+- [ ] `backend/src/api/routes/__init__.py`
+- [ ] `backend/src/api/routes/tracks.py` - GET /tracks, GET /tracks/{id}, DELETE /tracks/{id}
+- [ ] `backend/src/api/routes/search.py` - GET /search?q=...&genre=...&mood=...
+- [ ] `backend/src/api/routes/analyze.py` - POST /analyze/folder, GET /analyze/status/{job_id}
+- [ ] `backend/src/api/routes/audio.py` - GET /audio/stream/{id}, GET /audio/download/{id}, GET /audio/waveform/{id}
+- [ ] `backend/src/api/routes/similar.py` - GET /tracks/{id}/similar
+- [ ] `backend/src/api/routes/stats.py` - GET /stats (total tracks, genres distribution)
+
+### 1.6 Backend utils
+- [ ] `backend/src/utils/__init__.py`
+- [ ] `backend/src/utils/config.py` - Pydantic Settings for env vars
+- [ ] `backend/src/utils/logging.py` - Logging setup
+- [ ] `backend/src/utils/validators.py` - Audio file validation
+
+### 1.7 Frontend structure (Next.js 14)
+- [ ] `npx create-next-app@latest frontend --typescript --tailwind --app --no-src-dir`
+- [ ] `cd frontend && npm install`
+- [ ] Install deps: `shadcn-ui`, `@tanstack/react-query`, `zustand`, `axios`, `lucide-react`, `recharts`
+- [ ] `npx shadcn-ui@latest init`
+- [ ] Add shadcn components: button, input, slider, select, card, dialog, progress, toast
+
+### 1.8 Frontend structure details
+- [ ] `frontend/app/layout.tsx` - Root layout with QueryClientProvider
+- [ ] `frontend/app/page.tsx` - Main library view
+- [ ] `frontend/app/tracks/[id]/page.tsx` - Track detail page
+- [ ] `frontend/components/SearchBar.tsx`
+- [ ] `frontend/components/FilterPanel.tsx`
+- [ ] `frontend/components/TrackCard.tsx`
+- [ ] `frontend/components/TrackDetails.tsx`
+- [ ] `frontend/components/AudioPlayer.tsx`
+- [ ] `frontend/components/WaveformDisplay.tsx`
+- [ ] `frontend/components/BatchScanner.tsx`
+- [ ] `frontend/components/SimilarTracks.tsx`
+- [ ] `frontend/lib/api.ts` - Axios client with base URL
+- [ ] `frontend/lib/types.ts` - TypeScript interfaces
+- [ ] `frontend/hooks/useSearch.ts`
+- [ ] `frontend/hooks/useTracks.ts`
+- [ ] `frontend/hooks/useAudioPlayer.ts`
+- [ ] `frontend/.env.local.example`
+
+---
+
+## Phase 2: Database Schema & Migrations
+
+### 2.1 PostgreSQL setup
+- [ ] `docker-compose.yml`: service postgres with pgvector image `pgvector/pgvector:pg16`
+- [ ] Expose port 5432
+- [ ] Volume for persistence: `postgres_data:/var/lib/postgresql/data`
+- [ ] Init script: `backend/init-db.sql` with CREATE EXTENSION vector
+
+### 2.2 SQLAlchemy models
+- [ ] Define `AudioTrack` model in `schema.py`:
+  - id: UUID (PK)
+  - filepath: String (unique, indexed)
+  - filename: String
+  - duration_seconds: Float
+  - file_size_bytes: Integer
+  - format: String (mp3/wav)
+  - analyzed_at: DateTime
+  - tempo_bpm: Float
+  - key: String
+  - time_signature: String
+  - energy: Float
+  - danceability: Float
+  - valence: Float
+  - loudness_lufs: Float
+  - spectral_centroid: Float
+  - zero_crossing_rate: Float
+  - genre_primary: String (indexed)
+  - genre_secondary: ARRAY[String]
+  - genre_confidence: Float
+  - mood_primary: String (indexed)
+  - mood_secondary: ARRAY[String]
+  - mood_arousal: Float
+  - mood_valence: Float
+  - instruments: ARRAY[String]
+  - has_vocals: Boolean
+  - vocal_gender: String (nullable)
+  - embedding: Vector(512) (nullable, for future CLAP)
+  - embedding_model: String (nullable)
+  - metadata: JSON
+- [ ] Create indexes: filepath, genre_primary, mood_primary, tempo_bpm
+
+### 2.3 Alembic migrations
+- [ ] `alembic init backend/src/alembic`
+- [ ] Configure `alembic.ini` with DB URL
+- [ ] Create initial migration with schema above
+- [ ] Add pgvector extension in migration
+
+---
+
+## Phase 3: Core Audio Processing
+
+### 3.1 audio_processor.py - Librosa feature extraction
+- [ ] Function `load_audio(filepath: str) -> Tuple[np.ndarray, int]`
+- [ ] Function `extract_tempo(y, sr) -> float` - librosa.beat.tempo
+- [ ] Function `extract_key(y, sr) -> str` - librosa.feature.chroma_cqt + key detection
+- [ ] Function `extract_spectral_features(y, sr) -> dict`:
+  - spectral_centroid
+  - zero_crossing_rate
+  - spectral_rolloff
+  - spectral_bandwidth
+- [ ] Function `extract_mfcc(y, sr) -> np.ndarray`
+- [ ] Function `extract_chroma(y, sr) -> np.ndarray`
+- [ ] Function `extract_energy(y, sr) -> float` - RMS energy
+- [ ] Function `extract_all_features(filepath: str) -> dict` - orchestrator
+
+### 3.2 essentia_classifier.py - Essentia TensorFlow models
+- [ ] Download Essentia models (mtg-jamendo):
+  - genre: https://essentia.upf.edu/models/classification-heads/mtg_jamendo_genre/mtg_jamendo_genre-discogs-effnet-1.pb
+  - mood: https://essentia.upf.edu/models/classification-heads/mtg_jamendo_moodtheme/mtg_jamendo_moodtheme-discogs-effnet-1.pb
+  - instrument: https://essentia.upf.edu/models/classification-heads/mtg_jamendo_instrument/mtg_jamendo_instrument-discogs-effnet-1.pb
+- [ ] Store models in `backend/models/` directory
+- [ ] Class `EssentiaClassifier`:
+  - `__init__()`: load models
+  - `predict_genre(audio_path: str) -> dict`: returns {primary, secondary[], confidence}
+  - `predict_mood(audio_path: str) -> dict`: returns {primary, secondary[], arousal, valence}
+  - `predict_instruments(audio_path: str) -> List[dict]`: returns [{name, confidence}, ...]
+- [ ] Add model metadata files (class labels) in JSON
+
+### 3.3 waveform_generator.py
+- [ ] Function `generate_peaks(filepath: str, num_peaks: int = 800) -> List[float]`
+  - Load audio with librosa
+  - Downsample to num_peaks points
+  - Return normalized amplitude values
+- [ ] Cache peaks in JSON file next to audio (optional)
+
+### 3.4 file_scanner.py
+- [ ] Function `scan_folder(path: str, recursive: bool = True) -> List[str]`
+  - Walk directory tree
+  - Filter by extensions: .mp3, .wav, .flac, .m4a, .ogg
+  - Return list of absolute paths
+- [ ] Function `get_file_metadata(filepath: str) -> dict`
+  - Use mutagen for ID3 tags
+  - Return: filename, size, format
+
+### 3.5 analyzer.py - Main orchestrator
+- [ ] Class `AudioAnalyzer`:
+  - `__init__()`
+  - `analyze_file(filepath: str) -> AudioAnalysis`:
+    1. Validate file exists and is audio
+    2. Extract features (audio_processor)
+    3. Classify genre/mood/instruments (essentia_classifier)
+    4. Get file metadata (file_scanner)
+    5. Return structured AudioAnalysis object
+  - `analyze_folder(path: str, recursive: bool, progress_callback) -> List[AudioAnalysis]`:
+    - Scan folder
+    - Parallel processing with ThreadPoolExecutor (num_workers=4)
+    - Progress updates
+- [ ] Pydantic model `AudioAnalysis` matching JSON schema from architecture
+
+---
+
+## Phase 4: Database CRUD Operations
+
+### 4.1 crud.py - CRUD functions
+- [ ] `create_track(session, analysis: AudioAnalysis) -> AudioTrack`
+- [ ] `get_track_by_id(session, track_id: UUID) -> Optional[AudioTrack]`
+- [ ] `get_track_by_filepath(session, filepath: str) -> Optional[AudioTrack]`
+- [ ] `get_tracks(session, skip: int, limit: int, filters: dict) -> List[AudioTrack]`
+  - Support filters: genre, mood, bpm_min, bpm_max, energy_min, energy_max, has_vocals
+- [ ] `search_tracks(session, query: str, filters: dict, limit: int) -> List[AudioTrack]`
+  - Full-text search on: genre_primary, mood_primary, instruments, filename
+  - Combined with filters
+- [ ] `get_similar_tracks(session, track_id: UUID, limit: int) -> List[AudioTrack]`
+  - If embeddings exist: vector similarity with pgvector
+  - Fallback: similar genre + mood + BPM range
+- [ ] `delete_track(session, track_id: UUID) -> bool`
+- [ ] `get_stats(session) -> dict`
+  - Total tracks
+  - Genres distribution
+  - Moods distribution
+  - Average BPM
+  - Total duration
+
+---
+
+## Phase 5: FastAPI Backend Implementation
+
+### 5.1 config.py - Settings
+- [ ] `class Settings(BaseSettings)`:
+  - DATABASE_URL: str
+  - CORS_ORIGINS: List[str]
+  - ANALYSIS_USE_CLAP: bool = False
+  - ANALYSIS_NUM_WORKERS: int = 4
+  - ESSENTIA_MODELS_PATH: str
+  - AUDIO_LIBRARY_PATH: str (optional default scan path)
+- [ ] Load from `.env`
+
+### 5.2 main.py - FastAPI app
+- [ ] Create FastAPI app with metadata (title, version, description)
+- [ ] Add CORS middleware (allow frontend origin)
+- [ ] Add startup event: init DB engine, load Essentia models
+- [ ] Add shutdown event: cleanup
+- [ ] Include routers from routes/
+- [ ] Health check endpoint: GET /health
+
+### 5.3 routes/tracks.py
+- [ ] `GET /api/tracks`:
+  - Query params: skip, limit, genre, mood, bpm_min, bpm_max, energy_min, energy_max, has_vocals, sort_by
+  - Return paginated list of tracks
+  - Include total count
+- [ ] `GET /api/tracks/{track_id}`:
+  - Return full track details
+  - 404 if not found
+- [ ] `DELETE /api/tracks/{track_id}`:
+  - Soft delete or hard delete (remove from DB only, keep file)
+  - Return success
+
+### 5.4 routes/search.py
+- [ ] `GET /api/search`:
+  - Query params: q (search query), genre, mood, bpm_min, bpm_max, limit
+  - Full-text search + filters
+  - Return matching tracks
+
+### 5.5 routes/audio.py
+- [ ] `GET /api/audio/stream/{track_id}`:
+  - Get track from DB
+  - Return FileResponse with media_type audio/mpeg
+  - Support Range requests for seeking (Accept-Ranges: bytes)
+  - headers: Content-Disposition: inline
+- [ ] `GET /api/audio/download/{track_id}`:
+  - Same as stream but Content-Disposition: attachment
+- [ ] `GET /api/audio/waveform/{track_id}`:
+  - Get track from DB
+  - Generate or load cached peaks (waveform_generator)
+  - Return JSON: {peaks: [], duration: float}
+
+### 5.6 routes/analyze.py
+- [ ] `POST /api/analyze/folder`:
+  - Body: {path: str, recursive: bool}
+  - Validate path exists
+  - Start background job (asyncio Task or Celery)
+  - Return job_id
+- [ ] `GET /api/analyze/status/{job_id}`:
+  - Return job status: {status: "pending|running|completed|failed", progress: int, total: int, errors: []}
+- [ ] Background worker implementation:
+  - Scan folder
+  - For each file: analyze, save to DB (skip if already exists by filepath)
+  - Update job status
+  - Store job state in-memory dict or Redis
+
+### 5.7 routes/similar.py
+- [ ] `GET /api/tracks/{track_id}/similar`:
+  - Query params: limit (default 10)
+  - Get similar tracks (CRUD function)
+  - Return list of tracks
+
+### 5.8 routes/stats.py
+- [ ] `GET /api/stats`:
+  - Get stats (CRUD function)
+  - Return JSON with counts, distributions
+
+---
+
+## Phase 6: Frontend Implementation
+
+### 6.1 API client (lib/api.ts)
+- [ ] Create axios instance with baseURL from env var (NEXT_PUBLIC_API_URL)
+- [ ] API functions:
+  - `getTracks(params: FilterParams): Promise<{tracks: Track[], total: number}>`
+  - `getTrack(id: string): Promise<Track>`
+  - `deleteTrack(id: string): Promise<void>`
+  - `searchTracks(query: string, filters: FilterParams): Promise<Track[]>`
+  - `getSimilarTracks(id: string, limit: number): Promise<Track[]>`
+  - `analyzeFolder(path: string, recursive: boolean): Promise<{jobId: string}>`
+  - `getAnalyzeStatus(jobId: string): Promise<JobStatus>`
+  - `getStats(): Promise<Stats>`
+
+### 6.2 TypeScript types (lib/types.ts)
+- [ ] `interface Track` matching AudioTrack model
+- [ ] `interface FilterParams`
+- [ ] `interface JobStatus`
+- [ ] `interface Stats`
+
+### 6.3 Hooks
+- [ ] `hooks/useTracks.ts`:
+  - useQuery for fetching tracks with filters
+  - Pagination state
+  - Mutation for delete
+- [ ] `hooks/useSearch.ts`:
+  - Debounced search query
+  - Combined filters state
+- [ ] `hooks/useAudioPlayer.ts`:
+  - Current track state
+  - Play/pause/seek controls
+  - Volume control
+  - Queue management (optional)
+
+### 6.4 Components - UI primitives (shadcn)
+- [ ] Install shadcn components: button, input, slider, select, card, dialog, badge, progress, toast, dropdown-menu, tabs
+
+### 6.5 SearchBar.tsx
+- [ ] Input with search icon
+- [ ] Debounced onChange (300ms)
+- [ ] Clear button
+- [ ] Optional: suggestions dropdown
+
+### 6.6 FilterPanel.tsx
+- [ ] Genre multi-select (fetch available genres from API or hardcode)
+- [ ] Mood multi-select
+- [ ] BPM range slider (min/max)
+- [ ] Energy range slider
+- [ ] Has vocals checkbox
+- [ ] Sort by dropdown (Latest, BPM, Duration, Name)
+- [ ] Clear all filters button
+
+### 6.7 TrackCard.tsx
+- [ ] Props: track: Track, onPlay, onDelete
+- [ ] Display: filename, duration, BPM, genre, mood, instruments (badges)
+- [ ] Inline AudioPlayer component
+- [ ] Buttons: Play, Download, Similar, Details
+- [ ] Hover effects
+
+### 6.8 AudioPlayer.tsx
+- [ ] Props: trackId, filename, duration
+- [ ] HTML5 audio element with ref
+- [ ] WaveformDisplay child component
+- [ ] Progress slider (seek support)
+- [ ] Play/Pause button
+- [ ] Volume slider with icon
+- [ ] Time display (current / total)
+- [ ] Download button (calls /api/audio/download/{id})
+
+### 6.9 WaveformDisplay.tsx
+- [ ] Props: trackId, currentTime, duration
+- [ ] Fetch peaks from /api/audio/waveform/{id}
+- [ ] Canvas rendering:
+  - Draw bars for each peak
+  - Color played portion differently (blue vs gray)
+  - Click to seek
+- [ ] Loading state while fetching peaks
+
+### 6.10 TrackDetails.tsx (Modal/Dialog)
+- [ ] Props: trackId, open, onClose
+- [ ] Fetch full track details
+- [ ] Display all metadata in organized sections:
+  - Audio info: duration, format, file size
+  - Musical features: tempo, key, time signature, energy, danceability, valence
+  - Classification: genre (primary + secondary), mood (primary + secondary + arousal/valence), instruments
+  - Spectral features: spectral centroid, zero crossing rate, loudness
+- [ ] Similar tracks section (preview)
+- [ ] Download button
+
+### 6.11 SimilarTracks.tsx
+- [ ] Props: trackId, limit
+- [ ] Fetch similar tracks
+- [ ] Display as list of mini TrackCards
+- [ ] Click to navigate or play
+
+### 6.12 BatchScanner.tsx
+- [ ] Input for folder path
+- [ ] Recursive checkbox
+- [ ] Scan button
+- [ ] Progress bar (poll /api/analyze/status/{jobId})
+- [ ] Status messages (pending, running X/Y, completed, errors)
+- [ ] Error list if any
+
+### 6.13 Main page (app/page.tsx)
+- [ ] SearchBar at top
+- [ ] FilterPanel in sidebar or collapsible
+- [ ] BatchScanner in header or dedicated section
+- [ ] TrackCard grid/list
+- [ ] Pagination controls (Load More or page numbers)
+- [ ] Total tracks count
+- [ ] Loading states
+- [ ] Empty state if no tracks
+
+### 6.14 Track detail page (app/tracks/[id]/page.tsx)
+- [ ] Fetch track by ID
+- [ ] Large AudioPlayer
+- [ ] Full metadata display (similar to TrackDetails modal)
+- [ ] SimilarTracks section
+- [ ] Back to library button
+
+### 6.15 Layout (app/layout.tsx)
+- [ ] QueryClientProvider setup
+- [ ] Toast provider (for notifications)
+- [ ] Global styles
+- [ ] Header with app title and nav
+
+---
+
+## Phase 7: Docker & Deployment
+
+### 7.1 docker-compose.yml
+- [ ] Service: postgres
+  - image: pgvector/pgvector:pg16
+  - environment: POSTGRES_USER, POSTGRES_PASSWORD, POSTGRES_DB
+  - ports: 5432:5432
+  - volumes: postgres_data, init-db.sql
+- [ ] Service: backend
+  - build: ./backend
+  - depends_on: postgres
+  - environment: DATABASE_URL
+  - ports: 8000:8000
+  - volumes: audio files mount (read-only)
+- [ ] Service: frontend (optional, or dev mode only)
+  - build: ./frontend
+  - ports: 3000:3000
+  - environment: NEXT_PUBLIC_API_URL=http://localhost:8000
+
+### 7.2 Backend Dockerfile
+- [ ] FROM python:3.11-slim
+- [ ] Install system deps: ffmpeg, libsndfile1
+- [ ] COPY requirements.txt
+- [ ] RUN pip install -r requirements.txt
+- [ ] COPY src/
+- [ ] Download Essentia models during build or on startup
+- [ ] CMD: uvicorn src.api.main:app --host 0.0.0.0 --port 8000
+
+### 7.3 Frontend Dockerfile (production build)
+- [ ] FROM node:20-alpine
+- [ ] COPY package.json, package-lock.json
+- [ ] RUN npm ci
+- [ ] COPY app/, components/, lib/, hooks/, public/
+- [ ] RUN npm run build
+- [ ] CMD: npm start
+
+---
+
+## Phase 8: Documentation & Scripts
+
+### 8.1 Root README.md
+- [ ] Project description
+- [ ] Features list
+- [ ] Tech stack
+- [ ] Prerequisites (Docker, Node, Python)
+- [ ] Quick start:
+  - Clone repo
+  - Copy .env.example to .env
+  - docker-compose up
+  - Access frontend at localhost:3000
+- [ ] Development setup
+- [ ] API documentation link (FastAPI /docs)
+- [ ] Architecture diagram (optional)
+
+### 8.2 Backend README.md
+- [ ] Setup instructions
+- [ ] Environment variables documentation
+- [ ] Essentia models download instructions
+- [ ] API endpoints list
+- [ ] Database schema
+- [ ] Running migrations
+
+### 8.3 Frontend README.md
+- [ ] Setup instructions
+- [ ] Environment variables
+- [ ] Available scripts (dev, build, start)
+- [ ] Component structure
+
+### 8.4 Scripts
+- [ ] `scripts/download-essentia-models.sh` - Download Essentia models
+- [ ] `scripts/init-db.sh` - Run migrations
+- [ ] `backend/src/cli.py` - CLI for manual analysis (optional)
+
+---
+
+## Phase 9: Testing & Validation
+
+### 9.1 Backend tests (optional but recommended)
+- [ ] Test audio_processor.extract_all_features with sample file
+- [ ] Test essentia_classifier with sample file
+- [ ] Test CRUD operations
+- [ ] Test API endpoints with pytest + httpx
+
+### 9.2 Frontend tests (optional)
+- [ ] Test API client functions
+- [ ] Test hooks
+- [ ] Component tests with React Testing Library
+
+### 9.3 Integration test
+- [ ] Full flow: analyze folder -> save to DB -> search -> play -> download
+
+---
+
+## Phase 10: Optimizations & Polish
+
+### 10.1 Performance
+- [ ] Add database indexes
+- [ ] Cache waveform peaks
+- [ ] Optimize audio loading (lazy loading for large libraries)
+- [ ] Add compression for API responses
+
+### 10.2 UX improvements
+- [ ] Loading skeletons
+- [ ] Error boundaries
+- [ ] Toast notifications for actions
+- [ ] Keyboard shortcuts (space to play/pause, arrows to seek)
+- [ ] Dark mode support
+
+### 10.3 Backend improvements
+- [ ] Rate limiting
+- [ ] Request validation with Pydantic
+- [ ] Logging (structured logs)
+- [ ] Error handling middleware
+
+---
+
+## Implementation order priority
+
+1. **Phase 2** (Database) - Foundation
+2. **Phase 3** (Audio processing) - Core logic
+3. **Phase 4** (CRUD) - Data layer
+4. **Phase 5.1-5.2** (FastAPI setup) - API foundation
+5. **Phase 5.3-5.8** (API routes) - Complete backend
+6. **Phase 6.1-6.3** (Frontend setup + API client + hooks) - Frontend foundation
+7. **Phase 6.4-6.12** (Components) - UI implementation
+8. **Phase 6.13-6.15** (Pages) - Complete frontend
+9. **Phase 7** (Docker) - Deployment
+10. **Phase 8** (Documentation) - Final polish
+
+---
+
+## Notes for implementation
+
+- Use type hints everywhere in Python
+- Use TypeScript strict mode in frontend
+- Handle errors gracefully (try/catch, proper HTTP status codes)
+- Add logging at key points (file analysis start/end, DB operations)
+- Validate file paths (security: prevent path traversal)
+- Consider file locking for concurrent analysis
+- Add progress updates for long operations
+- Use environment variables for all config
+- Keep audio files outside Docker volumes for performance
+- Consider caching Essentia predictions (expensive)
+- Add retry logic for failed analyses
+- Support cancellation for long-running jobs
+
+## Files to download/prepare before starting
+
+1. Essentia models (3 files):
+   - mtg_jamendo_genre-discogs-effnet-1.pb
+   - mtg_jamendo_moodtheme-discogs-effnet-1.pb
+   - mtg_jamendo_instrument-discogs-effnet-1.pb
+2. Class labels JSON for each model
+3. Sample audio files for testing
+
+## External dependencies verification
+
+- librosa: check version compatibility with numpy
+- essentia-tensorflow: verify CPU-only build works
+- pgvector: verify PostgreSQL extension installation
+- FFmpeg: required by librosa for audio decoding
+
+## Security considerations
+
+- Validate all file paths (no ../ traversal)
+- Sanitize user input in search queries
+- Rate limit API endpoints
+- CORS: whitelist frontend origin only
+- Don't expose full filesystem paths in API responses
+- Consider adding authentication later (JWT)
+
+## Future enhancements (not in current scope)
+
+- CLAP embeddings for semantic search
+- Batch export to CSV/JSON
+- Playlist creation
+- Audio trimming/preview segments
+- Duplicate detection (audio fingerprinting)
+- Tag editing (write back to files)
+- Multi-user support with authentication
+- WebSocket for real-time analysis progress
+- Audio visualization (spectrogram, chromagram)
--- a/.env.example
+++ b/.env.example
@@ -0,0 +1,19 @@
+# Database
+DATABASE_URL=postgresql://audio_user:audio_password@localhost:5432/audio_classifier
+POSTGRES_USER=audio_user
+POSTGRES_PASSWORD=audio_password
+POSTGRES_DB=audio_classifier
+
+# Backend API
+CORS_ORIGINS=http://localhost:3000,http://127.0.0.1:3000
+API_HOST=0.0.0.0
+API_PORT=8000
+
+# Audio Analysis Configuration
+ANALYSIS_USE_CLAP=false
+ANALYSIS_NUM_WORKERS=4
+ESSENTIA_MODELS_PATH=/app/models
+AUDIO_LIBRARY_PATH=/path/to/your/audio/library
+
+# Frontend
+NEXT_PUBLIC_API_URL=http://localhost:8000
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,99 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+venv/
+ENV/
+env/
+.venv
+
+# FastAPI / Uvicorn
+*.log
+
+# Database
+*.db
+*.sqlite
+*.sqlite3
+
+# Alembic
+alembic.ini
+
+# Node
+node_modules/
+.pnp
+.pnp.js
+
+# Next.js
+.next/
+out/
+build/
+.vercel
+
+# Production
+/build
+
+# Misc
+.DS_Store
+*.pem
+
+# Debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+.pnpm-debug.log*
+
+# Local env files
+.env
+.env*.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# Docker
+postgres_data/
+
+# Essentia models (large files, download separately)
+backend/models/*.pb
+backend/models/*.json
+
+# Audio analysis cache
+*.peaks.json
+.audio_cache/
+
+# Testing
+.pytest_cache/
+coverage/
+*.cover
+.hypothesis/
+.coverage
+htmlcov/
+
+# MacOS
+.AppleDouble
+.LSOverride
+._*
--- a/QUICKSTART.md
+++ b/QUICKSTART.md
@@ -0,0 +1,193 @@
+# 🚀 Démarrage Rapide - Audio Classifier
+
+## En 5 minutes
+
+### 1. Configuration initiale
+
+```bash
+cd "/Users/benoit/Documents/code/Audio Classifier"
+
+# Copier les variables d'environnement
+cp .env.example .env
+
+# IMPORTANT : Éditer .env et définir votre chemin audio
+# AUDIO_LIBRARY_PATH=/Users/benoit/Music
+nano .env
+```
+
+### 2. Télécharger les modèles d'IA
+
+```bash
+./scripts/download-essentia-models.sh
+```
+
+Cela télécharge ~300 MB de modèles Essentia pour la classification.
+
+### 3. Lancer le backend
+
+```bash
+docker-compose up -d
+```
+
+Vérifier : http://localhost:8000/health
+
+### 4. Analyser votre bibliothèque
+
+```bash
+# Analyser un dossier (remplacer par votre chemin)
+curl -X POST http://localhost:8000/api/analyze/folder \
+  -H "Content-Type: application/json" \
+  -d '{"path": "/audio", "recursive": true}'
+
+# Note: "/audio" correspond à AUDIO_LIBRARY_PATH dans le conteneur
+```
+
+Vous recevrez un `job_id`. Suivre la progression :
+
+```bash
+curl http://localhost:8000/api/analyze/status/VOTRE_JOB_ID
+```
+
+### 5. Lancer le frontend
+
+```bash
+cd frontend
+cp .env.local.example .env.local
+npm install
+npm run dev
+```
+
+Ouvrir : http://localhost:3000
+
+## 📊 Exemples d'utilisation
+
+### Rechercher des pistes
+
+```bash
+# Par texte
+curl "http://localhost:8000/api/search?q=jazz"
+
+# Par genre
+curl "http://localhost:8000/api/tracks?genre=electronic&limit=10"
+
+# Par BPM
+curl "http://localhost:8000/api/tracks?bpm_min=120&bpm_max=140"
+
+# Par ambiance
+curl "http://localhost:8000/api/tracks?mood=energetic"
+```
+
+### Trouver des pistes similaires
+
+```bash
+# 1. Récupérer un track_id
+curl "http://localhost:8000/api/tracks?limit=1"
+
+# 2. Trouver des similaires
+curl "http://localhost:8000/api/tracks/TRACK_ID/similar?limit=10"
+```
+
+### Statistiques
+
+```bash
+curl "http://localhost:8000/api/stats"
+```
+
+### Écouter / Télécharger
+
+- Stream : http://localhost:8000/api/audio/stream/TRACK_ID
+- Download : http://localhost:8000/api/audio/download/TRACK_ID
+
+## 🎯 Ce qui est analysé
+
+Pour chaque fichier audio :
+
+✅ **Tempo** (BPM)
+✅ **Tonalité** (C major, D minor, etc.)
+✅ **Genre** (50 genres : electronic, jazz, rock, etc.)
+✅ **Ambiance** (56 moods : energetic, calm, dark, etc.)
+✅ **Instruments** (40 instruments : piano, guitar, drums, etc.)
+✅ **Énergie** (score 0-1)
+✅ **Danceability** (score 0-1)
+✅ **Valence** (positivité émotionnelle)
+✅ **Features spectrales** (centroid, zero-crossing, etc.)
+
+## ⚡ Performance
+
+**Sur CPU moderne (4 cores)** :
+
+- ~2-3 secondes par fichier
+- Analyse parallèle (4 workers par défaut)
+- 1000 fichiers ≈ 40-50 minutes
+
+**Pour accélérer** : Ajuster `ANALYSIS_NUM_WORKERS` dans `.env`
+
+## 📁 Structure
+
+```
+Audio Classifier/
+├── backend/          # API Python + analyse audio
+├── frontend/         # Interface Next.js
+├── scripts/          # Scripts utilitaires
+├── .env              # Configuration
+└── docker-compose.yml
+```
+
+## 🔍 Endpoints Principaux
+
+| Endpoint | Méthode | Description |
+|----------|---------|-------------|
+| `/api/tracks` | GET | Liste des pistes |
+| `/api/tracks/{id}` | GET | Détails piste |
+| `/api/search` | GET | Recherche textuelle |
+| `/api/tracks/{id}/similar` | GET | Pistes similaires |
+| `/api/analyze/folder` | POST | Lancer analyse |
+| `/api/audio/stream/{id}` | GET | Streaming audio |
+| `/api/audio/download/{id}` | GET | Télécharger |
+| `/api/stats` | GET | Statistiques |
+
+Documentation complète : http://localhost:8000/docs
+
+## 🐛 Problèmes Courants
+
+**"Connection refused"**
+```bash
+docker-compose ps  # Vérifier que les services sont up
+docker-compose logs backend  # Voir les erreurs
+```
+
+**"Model file not found"**
+```bash
+./scripts/download-essentia-models.sh
+ls backend/models/*.pb  # Vérifier présence
+```
+
+**Frontend ne charge pas**
+```bash
+cd frontend
+cat .env.local  # Vérifier NEXT_PUBLIC_API_URL
+npm install  # Réinstaller dépendances
+```
+
+## 📚 Documentation Complète
+
+- **[README.md](README.md)** - Vue d'ensemble du projet
+- **[SETUP.md](SETUP.md)** - Guide détaillé d'installation et configuration
+- **[.claude-todo.md](.claude-todo.md)** - Détails techniques d'implémentation
+
+## 🎵 Formats Supportés
+
+✅ MP3
+✅ WAV
+✅ FLAC
+✅ M4A
+✅ OGG
+
+## 💡 Prochaines Étapes
+
+1. **Analyser votre bibliothèque** : Lancer l'analyse sur vos fichiers
+2. **Explorer l'interface** : Naviguer dans les pistes analysées
+3. **Tester la recherche** : Filtrer par genre, BPM, mood
+4. **Découvrir les similaires** : Trouver des recommandations
+
+Enjoy! 🎶
--- a/README.md
+++ b/README.md
@@ -0,0 +1,241 @@
+# Audio Classifier
+
+Outil de classification audio automatique capable d'indexer et analyser des bibliothèques musicales entières.
+
+## 🎯 Fonctionnalités
+
+- **Analyse audio automatique** : Genre, instruments, tempo (BPM), tonalité, ambiance
+- **Classification intelligente** : Utilise Essentia + Librosa pour extraction de features
+- **Recherche avancée** : Filtres combinés (genre, mood, BPM, énergie) + recherche textuelle
+- **Lecteur audio intégré** : Prévisualisation avec waveform + téléchargement
+- **Base de données vectorielle** : PostgreSQL avec pgvector (prêt pour embeddings CLAP)
+- **100% local et CPU-only** : Aucune dépendance cloud, fonctionne sur CPU
+
+## 🛠 Stack Technique
+
+### Backend
+- **Python 3.11** + FastAPI (API REST async)
+- **Librosa** : Extraction features audio (tempo, spectral, chroma)
+- **Essentia-TensorFlow** : Classification genre/mood/instruments (modèles pré-entraînés)
+- **PostgreSQL + pgvector** : Base de données avec support vectoriel
+- **SQLAlchemy** : ORM
+
+### Frontend
+- **Next.js 14** + TypeScript
+- **TailwindCSS** + shadcn/ui
+- **React Query** : Gestion cache API
+- **Recharts** : Visualisations
+
+## 📋 Prérequis
+
+- **Docker** + Docker Compose (recommandé)
+- Ou manuellement :
+  - Python 3.11+
+  - Node.js 20+
+  - PostgreSQL 16 avec extension pgvector
+  - FFmpeg (pour librosa)
+
+## 🚀 Démarrage Rapide
+
+### 1. Cloner et configurer
+
+```bash
+git clone <repo>
+cd audio-classifier
+cp .env.example .env
+```
+
+### 2. Configurer l'environnement
+
+Éditer `.env` et définir le chemin vers votre bibliothèque audio :
+
+```env
+AUDIO_LIBRARY_PATH=/chemin/vers/vos/fichiers/audio
+```
+
+### 3. Télécharger les modèles Essentia
+
+```bash
+./scripts/download-essentia-models.sh
+```
+
+### 4. Lancer avec Docker
+
+```bash
+docker-compose up -d
+```
+
+L'API sera disponible sur `http://localhost:8000`
+La documentation interactive : `http://localhost:8000/docs`
+
+### 5. Lancer le frontend (développement)
+
+```bash
+cd frontend
+npm install
+npm run dev
+```
+
+Le frontend sera accessible sur `http://localhost:3000`
+
+## 📖 Utilisation
+
+### Scanner un dossier
+
+#### Via l'interface web
+1. Ouvrir `http://localhost:3000`
+2. Cliquer sur "Scan Folder"
+3. Entrer le chemin : `/audio/votre_dossier`
+4. Cocher "Recursive" si nécessaire
+5. Lancer l'analyse
+
+#### Via l'API
+```bash
+curl -X POST http://localhost:8000/api/analyze/folder \
+  -H "Content-Type: application/json" \
+  -d '{"path": "/audio/music", "recursive": true}'
+```
+
+### Rechercher des pistes
+
+- **Recherche textuelle** : Tapez dans la barre de recherche
+- **Filtres** : Genre, mood, BPM, énergie, instruments
+- **Similarité** : Cliquez sur "🔍 Similar" sur une piste
+
+### Écouter et télécharger
+
+- **Play** : Lecture directe dans le navigateur avec waveform
+- **Download** : Téléchargement du fichier original
+
+## 🏗 Architecture
+
+```
+audio-classifier/
+├── backend/              # API FastAPI
+│   ├── src/
+│   │   ├── core/        # Audio processing, classification
+│   │   ├── models/      # SQLAlchemy models, CRUD
+│   │   ├── api/         # Routes FastAPI
+│   │   └── utils/       # Config, logging
+│   └── models/          # Essentia models (.pb)
+│
+├── frontend/            # Next.js UI
+│   ├── app/            # Pages
+│   ├── components/     # React components
+│   ├── lib/            # API client, types
+│   └── hooks/          # React hooks
+│
+└── docker-compose.yml
+```
+
+## 🎼 Métadonnées Extraites
+
+### Features Audio
+- **Tempo** : BPM détecté
+- **Tonalité** : Clé musicale (C major, D minor, etc.)
+- **Signature rythmique** : 4/4, 3/4, etc.
+- **Énergie** : Intensité sonore (0-1)
+- **Valence** : Positivité/négativité (0-1)
+- **Danceability** : Dansabilité (0-1)
+- **Features spectrales** : Centroid, zero-crossing rate, rolloff
+
+### Classification
+- **Genre** : Primary + secondary (50 genres via Essentia)
+- **Mood** : Primary + secondary + arousal/valence (56 moods)
+- **Instruments** : Liste avec scores de confiance (40 instruments)
+- **Voix** : Présence, genre (futur)
+
+## 📊 API Endpoints
+
+### Tracks
+- `GET /api/tracks` - Liste des pistes avec filtres
+- `GET /api/tracks/{id}` - Détails d'une piste
+- `DELETE /api/tracks/{id}` - Supprimer une piste
+
+### Search
+- `GET /api/search?q=...&genre=...&mood=...` - Recherche
+
+### Audio
+- `GET /api/audio/stream/{id}` - Stream audio
+- `GET /api/audio/download/{id}` - Télécharger
+- `GET /api/audio/waveform/{id}` - Waveform data
+
+### Analysis
+- `POST /api/analyze/folder` - Scanner un dossier
+- `GET /api/analyze/status/{job_id}` - Statut d'analyse
+
+### Similar
+- `GET /api/tracks/{id}/similar` - Pistes similaires
+
+### Stats
+- `GET /api/stats` - Statistiques globales
+
+## ⚙️ Configuration Avancée
+
+### CPU-only vs GPU
+
+Par défaut, le système fonctionne en **CPU-only** pour compatibilité maximale.
+
+Pour activer CLAP embeddings (nécessite plus de RAM/temps) :
+```env
+ANALYSIS_USE_CLAP=true
+```
+
+### Parallélisation
+
+Ajuster le nombre de workers pour l'analyse :
+```env
+ANALYSIS_NUM_WORKERS=4  # Adapter selon votre CPU
+```
+
+### Formats supportés
+
+- WAV, MP3, FLAC, M4A, OGG
+
+## 🔧 Développement
+
+### Backend
+
+```bash
+cd backend
+python -m venv venv
+source venv/bin/activate  # Windows: venv\Scripts\activate
+pip install -r requirements.txt
+
+# Run migrations
+alembic upgrade head
+
+# Start dev server
+uvicorn src.api.main:app --reload --host 0.0.0.0 --port 8000
+```
+
+### Frontend
+
+```bash
+cd frontend
+npm install
+npm run dev
+```
+
+## 📝 TODO / Améliorations Futures
+
+- [ ] CLAP embeddings pour recherche sémantique ("calm piano for working")
+- [ ] Détection voix (homme/femme/choeur)
+- [ ] Export batch vers CSV/JSON
+- [ ] Création de playlists
+- [ ] Détection de doublons (audio fingerprinting)
+- [ ] Édition de tags (écriture dans les fichiers)
+- [ ] Authentication multi-utilisateurs
+- [ ] WebSocket pour progression temps réel
+
+## 📄 Licence
+
+MIT
+
+## 🤝 Contribution
+
+Les contributions sont les bienvenues ! Ouvrir une issue ou PR.
+
+## 📞 Support
+
+Pour toute question ou problème, ouvrir une issue GitHub.
--- a/SETUP.md
+++ b/SETUP.md
@@ -0,0 +1,403 @@
+# Audio Classifier - Guide de Déploiement
+
+## 📋 Prérequis
+
+- **Docker** & Docker Compose
+- **Node.js** 20+ (pour le frontend en mode dev)
+- **Python** 3.11+ (optionnel, si vous voulez tester le backend sans Docker)
+- **FFmpeg** (installé automatiquement dans le conteneur Docker)
+
+## 🚀 Installation Rapide
+
+### 1. Cloner le projet
+
+```bash
+cd "/Users/benoit/Documents/code/Audio Classifier"
+```
+
+### 2. Configurer les variables d'environnement
+
+```bash
+cp .env.example .env
+```
+
+Éditer `.env` et définir :
+
+```env
+# Chemin vers votre bibliothèque audio (IMPORTANT)
+AUDIO_LIBRARY_PATH=/chemin/absolu/vers/vos/fichiers/audio
+
+# Exemple macOS:
+# AUDIO_LIBRARY_PATH=/Users/benoit/Music
+
+# Le reste peut rester par défaut
+DATABASE_URL=postgresql://audio_user:audio_password@localhost:5432/audio_classifier
+```
+
+### 3. Télécharger les modèles Essentia
+
+Les modèles de classification sont nécessaires pour analyser les fichiers audio.
+
+```bash
+./scripts/download-essentia-models.sh
+```
+
+Cela télécharge (~300 MB) :
+- `mtg_jamendo_genre` : Classification de 50 genres musicaux
+- `mtg_jamendo_moodtheme` : Classification de 56 ambiances/moods
+- `mtg_jamendo_instrument` : Détection de 40 instruments
+
+### 4. Lancer le backend avec Docker
+
+```bash
+docker-compose up -d
+```
+
+Cela démarre :
+- **PostgreSQL** avec l'extension pgvector (port 5432)
+- **Backend FastAPI** (port 8000)
+
+Vérifier que tout fonctionne :
+
+```bash
+curl http://localhost:8000/health
+# Devrait retourner: {"status":"healthy",...}
+```
+
+Documentation API interactive : **http://localhost:8000/docs**
+
+### 5. Lancer le frontend (mode développement)
+
+```bash
+cd frontend
+cp .env.local.example .env.local
+npm install
+npm run dev
+```
+
+Frontend accessible sur : **http://localhost:3000**
+
+## 📊 Utiliser l'Application
+
+### Analyser votre bibliothèque audio
+
+**Option 1 : Via l'API (recommandé pour première analyse)**
+
+```bash
+curl -X POST http://localhost:8000/api/analyze/folder \
+  -H "Content-Type: application/json" \
+  -d '{
+    "path": "/audio",
+    "recursive": true
+  }'
+```
+
+**Note** : Le chemin `/audio` correspond au montage Docker de `AUDIO_LIBRARY_PATH`.
+
+Vous recevrez un `job_id`. Vérifier la progression :
+
+```bash
+curl http://localhost:8000/api/analyze/status/JOB_ID
+```
+
+**Option 2 : Via Python (backend local)**
+
+```bash
+cd backend
+python -m venv venv
+source venv/bin/activate  # Windows: venv\Scripts\activate
+pip install -r requirements.txt
+
+# Analyser un fichier
+python -c "
+from src.core.analyzer import AudioAnalyzer
+analyzer = AudioAnalyzer()
+result = analyzer.analyze_file('/path/to/audio.mp3')
+print(result)
+"
+```
+
+### Rechercher des pistes
+
+**Par texte :**
+
+```bash
+curl "http://localhost:8000/api/search?q=jazz&limit=10"
+```
+
+**Avec filtres :**
+
+```bash
+curl "http://localhost:8000/api/tracks?genre=electronic&bpm_min=120&bpm_max=140&limit=20"
+```
+
+**Pistes similaires :**
+
+```bash
+curl "http://localhost:8000/api/tracks/TRACK_ID/similar?limit=10"
+```
+
+### Télécharger / Écouter
+
+- **Stream** : `http://localhost:8000/api/audio/stream/TRACK_ID`
+- **Download** : `http://localhost:8000/api/audio/download/TRACK_ID`
+- **Waveform** : `http://localhost:8000/api/audio/waveform/TRACK_ID`
+
+## 🏗️ Architecture
+
+```
+audio-classifier/
+├── backend/                  # API Python FastAPI
+│   ├── src/
+│   │   ├── core/            # Audio processing
+│   │   │   ├── audio_processor.py      # Librosa features
+│   │   │   ├── essentia_classifier.py  # Genre/Mood/Instruments
+│   │   │   ├── waveform_generator.py   # Peaks pour UI
+│   │   │   ├── file_scanner.py         # Scan dossiers
+│   │   │   └── analyzer.py             # Orchestrateur
+│   │   ├── models/          # Database
+│   │   │   ├── schema.py               # SQLAlchemy models
+│   │   │   └── crud.py                 # CRUD operations
+│   │   ├── api/             # FastAPI routes
+│   │   │   └── routes/
+│   │   │       ├── tracks.py           # GET/DELETE tracks
+│   │   │       ├── search.py           # Recherche
+│   │   │       ├── audio.py            # Stream/Download
+│   │   │       ├── analyze.py          # Jobs d'analyse
+│   │   │       ├── similar.py          # Recommandations
+│   │   │       └── stats.py            # Statistiques
+│   │   └── utils/           # Config, logging, validators
+│   ├── models/              # Essentia .pb files
+│   └── requirements.txt
+│
+├── frontend/                # UI Next.js
+│   ├── app/
+│   │   ├── page.tsx        # Page principale
+│   │   └── layout.tsx
+│   ├── components/
+│   │   └── providers/
+│   ├── lib/
+│   │   ├── api.ts          # Client API
+│   │   ├── types.ts        # TypeScript types
+│   │   └── utils.ts        # Helpers
+│   └── package.json
+│
+├── scripts/
+│   └── download-essentia-models.sh
+│
+└── docker-compose.yml
+```
+
+## 🔧 Configuration Avancée
+
+### Performance CPU
+
+Le système est optimisé pour CPU-only. Sur un CPU moderne (4 cores) :
+
+- **Librosa features** : ~0.5-1s par fichier
+- **Essentia classification** : ~1-2s par fichier
+- **Total** : ~2-3s par fichier
+
+Ajuster le parallélisme dans `.env` :
+
+```env
+ANALYSIS_NUM_WORKERS=4  # Nombre de threads parallèles
+```
+
+### Activer les embeddings CLAP (optionnel)
+
+Pour la recherche sémantique avancée ("calm piano for working") :
+
+```env
+ANALYSIS_USE_CLAP=true
+```
+
+**Attention** : Augmente significativement le temps d'analyse (~5-10s supplémentaires par fichier).
+
+### Base de données
+
+Par défaut, PostgreSQL tourne dans Docker. Pour utiliser une DB externe :
+
+```env
+DATABASE_URL=postgresql://user:pass@external-host:5432/dbname
+```
+
+Appliquer les migrations :
+
+```bash
+cd backend
+alembic upgrade head
+```
+
+## 📊 Données Extraites
+
+### Features Audio (Librosa)
+- **Tempo** : BPM détecté automatiquement
+- **Tonalité** : Clé musicale (C major, D minor, etc.)
+- **Signature rythmique** : 4/4, 3/4, etc.
+- **Énergie** : Intensité sonore (0-1)
+- **Danceability** : Score de dansabilité (0-1)
+- **Valence** : Positivité/négativité émotionnelle (0-1)
+- **Features spectrales** : Centroid, rolloff, bandwidth
+
+### Classification (Essentia)
+- **Genre** : 50 genres possibles (rock, electronic, jazz, etc.)
+- **Mood** : 56 ambiances (energetic, calm, dark, happy, etc.)
+- **Instruments** : 40 instruments détectables (piano, guitar, drums, etc.)
+
+## 🐛 Troubleshooting
+
+### Le backend ne démarre pas
+
+```bash
+docker-compose logs backend
+```
+
+Vérifier que :
+- PostgreSQL est bien démarré (`docker-compose ps`)
+- Les modèles Essentia sont téléchargés (`ls backend/models/*.pb`)
+- Le port 8000 n'est pas déjà utilisé
+
+### "Model file not found"
+
+```bash
+./scripts/download-essentia-models.sh
+```
+
+### Frontend ne se connecte pas au backend
+
+Vérifier `.env.local` :
+
+```env
+NEXT_PUBLIC_API_URL=http://localhost:8000
+```
+
+### Analyse très lente
+
+- Réduire `ANALYSIS_NUM_WORKERS` si CPU surchargé
+- Désactiver `ANALYSIS_USE_CLAP` si activé
+- Vérifier que les fichiers audio sont accessibles rapidement (éviter NAS lents)
+
+### Erreur FFmpeg
+
+FFmpeg est installé automatiquement dans le conteneur Docker. Si vous lancez le backend en local :
+
+```bash
+# macOS
+brew install ffmpeg
+
+# Ubuntu/Debian
+sudo apt-get install ffmpeg libsndfile1
+```
+
+## 📦 Production
+
+### Build frontend
+
+```bash
+cd frontend
+npm run build
+npm start  # Port 3000
+```
+
+### Backend en production
+
+Utiliser Gunicorn avec Uvicorn workers :
+
+```bash
+pip install gunicorn
+gunicorn src.api.main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000
+```
+
+### Reverse proxy (Nginx)
+
+```nginx
+server {
+    listen 80;
+    server_name your-domain.com;
+
+    location /api {
+        proxy_pass http://localhost:8000;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+    }
+
+    location / {
+        proxy_pass http://localhost:3000;
+    }
+}
+```
+
+## 🔒 Sécurité
+
+**IMPORTANT** : Le système actuel n'a PAS d'authentification.
+
+Pour la production :
+- Ajouter authentication JWT
+- Limiter l'accès aux endpoints d'analyse
+- Valider tous les chemins de fichiers (déjà fait côté backend)
+- Utiliser HTTPS
+- Restreindre CORS aux domaines autorisés
+
+## 📝 Développement
+
+### Ajouter un nouveau genre/mood
+
+Éditer `backend/src/core/essentia_classifier.py` :
+
+```python
+self.class_labels["genre"] = [
+    # ... genres existants
+    "nouveau_genre",
+]
+```
+
+### Modifier les features extraites
+
+Éditer `backend/src/core/audio_processor.py` et ajouter votre fonction :
+
+```python
+def extract_new_feature(y, sr) -> float:
+    # Votre logique
+    return feature_value
+```
+
+Puis mettre à jour `extract_all_features()`.
+
+### Ajouter une route API
+
+1. Créer `backend/src/api/routes/nouvelle_route.py`
+2. Ajouter le router dans `backend/src/api/main.py`
+
+### Tests
+
+```bash
+# Backend
+cd backend
+pytest
+
+# Frontend
+cd frontend
+npm test
+```
+
+## 📈 Améliorations Futures
+
+- [ ] Interface de scan dans le frontend (actuellement via API seulement)
+- [ ] Player audio intégré avec waveform interactive
+- [ ] Filtres avancés (multi-genre, range sliders)
+- [ ] Export playlists (M3U, CSV, JSON)
+- [ ] Détection de doublons (audio fingerprinting)
+- [ ] Édition de tags ID3
+- [ ] Recherche sémantique avec CLAP
+- [ ] Authentication multi-utilisateurs
+- [ ] WebSocket pour progression temps réel
+
+## 🆘 Support
+
+Pour toute question :
+1. Vérifier les logs : `docker-compose logs -f backend`
+2. Consulter la doc API : http://localhost:8000/docs
+3. Ouvrir une issue GitHub
+
+Bon classement ! 🎵
--- a/backend/.env.example
+++ b/backend/.env.example
@@ -0,0 +1,13 @@
+# Database
+DATABASE_URL=postgresql://audio_user:audio_password@localhost:5432/audio_classifier
+
+# API Configuration
+CORS_ORIGINS=http://localhost:3000,http://127.0.0.1:3000
+
+# Audio Analysis
+ANALYSIS_USE_CLAP=false
+ANALYSIS_NUM_WORKERS=4
+ESSENTIA_MODELS_PATH=./models
+
+# Audio Library
+AUDIO_LIBRARY_PATH=/path/to/your/audio/library
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -0,0 +1,34 @@
+FROM python:3.11-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    libsndfile1 \
+    libsndfile1-dev \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY src/ ./src/
+COPY alembic.ini .
+COPY models/ ./models/
+
+# Create models directory if not exists
+RUN mkdir -p /app/models
+
+# Expose port
+EXPOSE 8000
+
+# Run migrations and start server
+CMD alembic upgrade head && \
+    uvicorn src.api.main:app --host 0.0.0.0 --port 8000
--- a/backend/init-db.sql
+++ b/backend/init-db.sql
@@ -0,0 +1,5 @@
+-- Enable pgvector extension
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- Create UUID extension
+CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -0,0 +1,30 @@
+# Web Framework
+fastapi==0.109.0
+uvicorn[standard]==0.27.0
+python-multipart==0.0.6
+
+# Database
+sqlalchemy==2.0.25
+psycopg2-binary==2.9.9
+pgvector==0.2.4
+alembic==1.13.1
+
+# Audio Processing
+librosa==0.10.1
+essentia-tensorflow==2.1b6.dev1110
+soundfile==0.12.1
+audioread==3.0.1
+mutagen==1.47.0
+
+# Scientific Computing
+numpy==1.24.3
+scipy==1.11.4
+
+# Configuration & Validation
+pydantic==2.5.3
+pydantic-settings==2.1.0
+python-dotenv==1.0.0
+
+# Utilities
+aiofiles==23.2.1
+httpx==0.26.0
--- a/backend/src/init.py
+++ b/backend/src/init.py
--- a/backend/src/alembic/env.py
+++ b/backend/src/alembic/env.py
@@ -0,0 +1,85 @@
+"""Alembic environment configuration."""
+from logging.config import fileConfig
+
+from sqlalchemy import engine_from_config
+from sqlalchemy import pool
+
+from alembic import context
+
+# Import your models
+from src.models.database import Base
+from src.models.schema import AudioTrack  # Import all models
+from src.utils.config import settings
+
+# this is the Alembic Config object, which provides
+# access to the values within the .ini file in use.
+config = context.config
+
+# Override sqlalchemy.url with our settings
+config.set_main_option("sqlalchemy.url", settings.DATABASE_URL)
+
+# Interpret the config file for Python logging.
+# This line sets up loggers basically.
+if config.config_file_name is not None:
+    fileConfig(config.config_file_name)
+
+# add your model's MetaData object here
+# for 'autogenerate' support
+target_metadata = Base.metadata
+
+# other values from the config, defined by the needs of env.py,
+# can be acquired:
+# my_important_option = config.get_main_option("my_important_option")
+# ... etc.
+
+
+def run_migrations_offline() -> None:
+    """Run migrations in 'offline' mode.
+
+    This configures the context with just a URL
+    and not an Engine, though an Engine is acceptable
+    here as well.  By skipping the Engine creation
+    we don't even need a DBAPI to be available.
+
+    Calls to context.execute() here emit the given string to the
+    script output.
+
+    """
+    url = config.get_main_option("sqlalchemy.url")
+    context.configure(
+        url=url,
+        target_metadata=target_metadata,
+        literal_binds=True,
+        dialect_opts={"paramstyle": "named"},
+    )
+
+    with context.begin_transaction():
+        context.run_migrations()
+
+
+def run_migrations_online() -> None:
+    """Run migrations in 'online' mode.
+
+    In this scenario we need to create an Engine
+    and associate a connection with the context.
+
+    """
+    connectable = engine_from_config(
+        config.get_section(config.config_ini_section, {}),
+        prefix="sqlalchemy.",
+        poolclass=pool.NullPool,
+    )
+
+    with connectable.connect() as connection:
+        context.configure(
+            connection=connection, target_metadata=target_metadata
+        )
+
+        with context.begin_transaction():
+            context.run_migrations()
+
+
+if context.is_offline_mode():
+    run_migrations_offline()
+else:
+    run_migrations_online()
--- a/backend/src/alembic/script.py.mako
+++ b/backend/src/alembic/script.py.mako
@@ -0,0 +1,26 @@
+"""${message}
+
+Revision ID: ${up_revision}
+Revises: ${down_revision | comma,n}
+Create Date: ${create_date}
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+${imports if imports else ""}
+
+# revision identifiers, used by Alembic.
+revision: str = ${repr(up_revision)}
+down_revision: Union[str, None] = ${repr(down_revision)}
+branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
+depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
+
+
+def upgrade() -> None:
+    ${upgrades if upgrades else "pass"}
+
+
+def downgrade() -> None:
+    ${downgrades if downgrades else "pass"}
--- a/backend/src/alembic/versions/20251127_001_initial_schema.py
+++ b/backend/src/alembic/versions/20251127_001_initial_schema.py
@@ -0,0 +1,97 @@
+"""Initial schema with audio_tracks table
+
+Revision ID: 001
+Revises:
+Create Date: 2025-11-27
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+from pgvector.sqlalchemy import Vector
+
+# revision identifiers, used by Alembic.
+revision: str = '001'
+down_revision: Union[str, None] = None
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Create pgvector extension
+    op.execute('CREATE EXTENSION IF NOT EXISTS vector')
+    op.execute('CREATE EXTENSION IF NOT EXISTS "uuid-ossp"')
+
+    # Create audio_tracks table
+    op.create_table(
+        'audio_tracks',
+        sa.Column('id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
+        sa.Column('filepath', sa.String(), nullable=False),
+        sa.Column('filename', sa.String(), nullable=False),
+        sa.Column('duration_seconds', sa.Float(), nullable=True),
+        sa.Column('file_size_bytes', sa.BigInteger(), nullable=True),
+        sa.Column('format', sa.String(), nullable=True),
+        sa.Column('analyzed_at', sa.DateTime(), nullable=False, server_default=sa.text('now()')),
+
+        # Musical features
+        sa.Column('tempo_bpm', sa.Float(), nullable=True),
+        sa.Column('key', sa.String(), nullable=True),
+        sa.Column('time_signature', sa.String(), nullable=True),
+        sa.Column('energy', sa.Float(), nullable=True),
+        sa.Column('danceability', sa.Float(), nullable=True),
+        sa.Column('valence', sa.Float(), nullable=True),
+        sa.Column('loudness_lufs', sa.Float(), nullable=True),
+        sa.Column('spectral_centroid', sa.Float(), nullable=True),
+        sa.Column('zero_crossing_rate', sa.Float(), nullable=True),
+
+        # Genre classification
+        sa.Column('genre_primary', sa.String(), nullable=True),
+        sa.Column('genre_secondary', postgresql.ARRAY(sa.String()), nullable=True),
+        sa.Column('genre_confidence', sa.Float(), nullable=True),
+
+        # Mood classification
+        sa.Column('mood_primary', sa.String(), nullable=True),
+        sa.Column('mood_secondary', postgresql.ARRAY(sa.String()), nullable=True),
+        sa.Column('mood_arousal', sa.Float(), nullable=True),
+        sa.Column('mood_valence', sa.Float(), nullable=True),
+
+        # Instruments
+        sa.Column('instruments', postgresql.ARRAY(sa.String()), nullable=True),
+
+        # Vocals
+        sa.Column('has_vocals', sa.Boolean(), nullable=True),
+        sa.Column('vocal_gender', sa.String(), nullable=True),
+
+        # Embeddings
+        sa.Column('embedding', Vector(512), nullable=True),
+        sa.Column('embedding_model', sa.String(), nullable=True),
+
+        # Metadata
+        sa.Column('metadata', postgresql.JSON(astext_type=sa.Text()), nullable=True),
+
+        sa.PrimaryKeyConstraint('id')
+    )
+
+    # Create indexes
+    op.create_index('idx_filepath', 'audio_tracks', ['filepath'], unique=True)
+    op.create_index('idx_genre_primary', 'audio_tracks', ['genre_primary'])
+    op.create_index('idx_mood_primary', 'audio_tracks', ['mood_primary'])
+    op.create_index('idx_tempo_bpm', 'audio_tracks', ['tempo_bpm'])
+
+    # Create vector index for similarity search (IVFFlat)
+    # Note: This requires some data in the table to train the index
+    # For now, we'll create it later when we have embeddings
+    # op.execute(
+    #     "CREATE INDEX idx_embedding ON audio_tracks USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100)"
+    # )
+
+
+def downgrade() -> None:
+    op.drop_index('idx_tempo_bpm', table_name='audio_tracks')
+    op.drop_index('idx_mood_primary', table_name='audio_tracks')
+    op.drop_index('idx_genre_primary', table_name='audio_tracks')
+    op.drop_index('idx_filepath', table_name='audio_tracks')
+    op.drop_table('audio_tracks')
+    op.execute('DROP EXTENSION IF EXISTS vector')
--- a/backend/src/api/init.py
+++ b/backend/src/api/init.py
--- a/backend/src/api/main.py
+++ b/backend/src/api/main.py
@@ -0,0 +1,81 @@
+"""FastAPI main application."""
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from contextlib import asynccontextmanager
+
+from ..utils.config import settings
+from ..utils.logging import setup_logging, get_logger
+from ..models.database import engine, Base
+
+# Import routes
+from .routes import tracks, search, audio, analyze, similar, stats
+
+# Setup logging
+setup_logging()
+logger = get_logger(__name__)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Application lifespan events."""
+    # Startup
+    logger.info("Starting Audio Classifier API")
+    logger.info(f"Database: {settings.DATABASE_URL.split('@')[-1]}")  # Hide credentials
+    logger.info(f"CORS origins: {settings.cors_origins_list}")
+
+    # Create tables (in production, use Alembic migrations)
+    # Base.metadata.create_all(bind=engine)
+
+    yield
+
+    # Shutdown
+    logger.info("Shutting down Audio Classifier API")
+
+
+# Create FastAPI app
+app = FastAPI(
+    title=settings.APP_NAME,
+    version=settings.APP_VERSION,
+    description="Audio classification and analysis API",
+    lifespan=lifespan,
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.cors_origins_list,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# Health check
+@app.get("/health", tags=["health"])
+async def health_check():
+    """Health check endpoint."""
+    return {
+        "status": "healthy",
+        "version": settings.APP_VERSION,
+        "service": settings.APP_NAME,
+    }
+
+
+# Include routers
+app.include_router(tracks.router, prefix="/api/tracks", tags=["tracks"])
+app.include_router(search.router, prefix="/api/search", tags=["search"])
+app.include_router(audio.router, prefix="/api/audio", tags=["audio"])
+app.include_router(analyze.router, prefix="/api/analyze", tags=["analyze"])
+app.include_router(similar.router, prefix="/api", tags=["similar"])
+app.include_router(stats.router, prefix="/api/stats", tags=["stats"])
+
+
+@app.get("/", tags=["root"])
+async def root():
+    """Root endpoint."""
+    return {
+        "message": "Audio Classifier API",
+        "version": settings.APP_VERSION,
+        "docs": "/docs",
+        "health": "/health",
+    }
--- a/backend/src/api/routes/init.py
+++ b/backend/src/api/routes/init.py
--- a/backend/src/api/routes/analyze.py
+++ b/backend/src/api/routes/analyze.py
@@ -0,0 +1,217 @@
+"""Analysis job endpoints."""
+from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks
+from sqlalchemy.orm import Session
+from pydantic import BaseModel
+from typing import Dict, Optional
+from uuid import uuid4
+import asyncio
+
+from ...models.database import get_db
+from ...models import crud
+from ...core.analyzer import AudioAnalyzer
+from ...utils.logging import get_logger
+from ...utils.validators import validate_directory_path
+
+router = APIRouter()
+logger = get_logger(__name__)
+
+# In-memory job storage (in production, use Redis)
+jobs: Dict[str, dict] = {}
+
+
+class AnalyzeFolderRequest(BaseModel):
+    """Request to analyze a folder."""
+    path: str
+    recursive: bool = True
+
+
+class JobStatus(BaseModel):
+    """Analysis job status."""
+    job_id: str
+    status: str  # pending, running, completed, failed
+    progress: int
+    total: int
+    current_file: Optional[str] = None
+    errors: list = []
+
+
+def analyze_folder_task(job_id: str, path: str, recursive: bool, db_url: str):
+    """Background task to analyze folder.
+
+    Args:
+        job_id: Job UUID
+        path: Directory path
+        recursive: Scan recursively
+        db_url: Database URL for new session
+    """
+    from ...models.database import SessionLocal
+
+    try:
+        logger.info(f"Starting analysis job {job_id} for {path}")
+
+        # Update job status
+        jobs[job_id]["status"] = "running"
+
+        # Create analyzer
+        analyzer = AudioAnalyzer()
+
+        # Progress callback
+        def progress_callback(current: int, total: int, filename: str):
+            jobs[job_id]["progress"] = current
+            jobs[job_id]["total"] = total
+            jobs[job_id]["current_file"] = filename
+
+        # Analyze folder
+        results = analyzer.analyze_folder(
+            path=path,
+            recursive=recursive,
+            progress_callback=progress_callback,
+        )
+
+        # Save to database
+        db = SessionLocal()
+        try:
+            saved_count = 0
+            for analysis in results:
+                try:
+                    crud.upsert_track(db, analysis)
+                    saved_count += 1
+                except Exception as e:
+                    logger.error(f"Failed to save track {analysis.filename}: {e}")
+                    jobs[job_id]["errors"].append({
+                        "file": analysis.filename,
+                        "error": str(e)
+                    })
+
+            logger.info(f"Job {job_id} completed: {saved_count}/{len(results)} tracks saved")
+
+            # Update job status
+            jobs[job_id]["status"] = "completed"
+            jobs[job_id]["progress"] = len(results)
+            jobs[job_id]["total"] = len(results)
+            jobs[job_id]["current_file"] = None
+            jobs[job_id]["saved_count"] = saved_count
+
+        finally:
+            db.close()
+
+    except Exception as e:
+        logger.error(f"Job {job_id} failed: {e}")
+        jobs[job_id]["status"] = "failed"
+        jobs[job_id]["errors"].append({
+            "error": str(e)
+        })
+
+
+@router.post("/folder")
+async def analyze_folder(
+    request: AnalyzeFolderRequest,
+    background_tasks: BackgroundTasks,
+    db: Session = Depends(get_db),
+):
+    """Start folder analysis job.
+
+    Args:
+        request: Folder analysis request
+        background_tasks: FastAPI background tasks
+        db: Database session
+
+    Returns:
+        Job ID for status tracking
+
+    Raises:
+        HTTPException: 400 if path is invalid
+    """
+    # Validate path
+    validated_path = validate_directory_path(request.path)
+
+    if not validated_path:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid or inaccessible directory: {request.path}"
+        )
+
+    # Create job
+    job_id = str(uuid4())
+
+    jobs[job_id] = {
+        "job_id": job_id,
+        "status": "pending",
+        "progress": 0,
+        "total": 0,
+        "current_file": None,
+        "errors": [],
+        "path": validated_path,
+        "recursive": request.recursive,
+    }
+
+    # Get database URL for background task
+    from ...utils.config import settings
+
+    # Start background task
+    background_tasks.add_task(
+        analyze_folder_task,
+        job_id,
+        validated_path,
+        request.recursive,
+        settings.DATABASE_URL,
+    )
+
+    logger.info(f"Created analysis job {job_id} for {validated_path}")
+
+    return {
+        "job_id": job_id,
+        "message": "Analysis job started",
+        "path": validated_path,
+        "recursive": request.recursive,
+    }
+
+
+@router.get("/status/{job_id}")
+async def get_job_status(job_id: str):
+    """Get analysis job status.
+
+    Args:
+        job_id: Job UUID
+
+    Returns:
+        Job status
+
+    Raises:
+        HTTPException: 404 if job not found
+    """
+    if job_id not in jobs:
+        raise HTTPException(status_code=404, detail="Job not found")
+
+    job_data = jobs[job_id]
+
+    return {
+        "job_id": job_data["job_id"],
+        "status": job_data["status"],
+        "progress": job_data["progress"],
+        "total": job_data["total"],
+        "current_file": job_data.get("current_file"),
+        "errors": job_data.get("errors", []),
+        "saved_count": job_data.get("saved_count"),
+    }
+
+
+@router.delete("/job/{job_id}")
+async def delete_job(job_id: str):
+    """Delete job from memory.
+
+    Args:
+        job_id: Job UUID
+
+    Returns:
+        Success message
+
+    Raises:
+        HTTPException: 404 if job not found
+    """
+    if job_id not in jobs:
+        raise HTTPException(status_code=404, detail="Job not found")
+
+    del jobs[job_id]
+
+    return {"message": "Job deleted", "job_id": job_id}
--- a/backend/src/api/routes/audio.py
+++ b/backend/src/api/routes/audio.py
@@ -0,0 +1,152 @@
+"""Audio streaming and download endpoints."""
+from fastapi import APIRouter, Depends, HTTPException, Request
+from fastapi.responses import FileResponse
+from sqlalchemy.orm import Session
+from uuid import UUID
+from pathlib import Path
+
+from ...models.database import get_db
+from ...models import crud
+from ...core.waveform_generator import get_waveform_data
+from ...utils.logging import get_logger
+
+router = APIRouter()
+logger = get_logger(__name__)
+
+
+@router.get("/stream/{track_id}")
+async def stream_audio(
+    track_id: UUID,
+    request: Request,
+    db: Session = Depends(get_db),
+):
+    """Stream audio file with range request support.
+
+    Args:
+        track_id: Track UUID
+        request: HTTP request
+        db: Database session
+
+    Returns:
+        Audio file for streaming
+
+    Raises:
+        HTTPException: 404 if track not found or file doesn't exist
+    """
+    track = crud.get_track_by_id(db, track_id)
+
+    if not track:
+        raise HTTPException(status_code=404, detail="Track not found")
+
+    file_path = Path(track.filepath)
+
+    if not file_path.exists():
+        logger.error(f"File not found: {track.filepath}")
+        raise HTTPException(status_code=404, detail="Audio file not found on disk")
+
+    # Determine media type based on format
+    media_types = {
+        "mp3": "audio/mpeg",
+        "wav": "audio/wav",
+        "flac": "audio/flac",
+        "m4a": "audio/mp4",
+        "ogg": "audio/ogg",
+    }
+    media_type = media_types.get(track.format, "audio/mpeg")
+
+    return FileResponse(
+        path=str(file_path),
+        media_type=media_type,
+        filename=track.filename,
+        headers={
+            "Accept-Ranges": "bytes",
+            "Content-Disposition": f'inline; filename="{track.filename}"',
+        },
+    )
+
+
+@router.get("/download/{track_id}")
+async def download_audio(
+    track_id: UUID,
+    db: Session = Depends(get_db),
+):
+    """Download audio file.
+
+    Args:
+        track_id: Track UUID
+        db: Database session
+
+    Returns:
+        Audio file for download
+
+    Raises:
+        HTTPException: 404 if track not found or file doesn't exist
+    """
+    track = crud.get_track_by_id(db, track_id)
+
+    if not track:
+        raise HTTPException(status_code=404, detail="Track not found")
+
+    file_path = Path(track.filepath)
+
+    if not file_path.exists():
+        logger.error(f"File not found: {track.filepath}")
+        raise HTTPException(status_code=404, detail="Audio file not found on disk")
+
+    # Determine media type
+    media_types = {
+        "mp3": "audio/mpeg",
+        "wav": "audio/wav",
+        "flac": "audio/flac",
+        "m4a": "audio/mp4",
+        "ogg": "audio/ogg",
+    }
+    media_type = media_types.get(track.format, "audio/mpeg")
+
+    return FileResponse(
+        path=str(file_path),
+        media_type=media_type,
+        filename=track.filename,
+        headers={
+            "Content-Disposition": f'attachment; filename="{track.filename}"',
+        },
+    )
+
+
+@router.get("/waveform/{track_id}")
+async def get_waveform(
+    track_id: UUID,
+    num_peaks: int = 800,
+    db: Session = Depends(get_db),
+):
+    """Get waveform peak data for visualization.
+
+    Args:
+        track_id: Track UUID
+        num_peaks: Number of peaks to generate
+        db: Database session
+
+    Returns:
+        Waveform data with peaks and duration
+
+    Raises:
+        HTTPException: 404 if track not found or file doesn't exist
+    """
+    track = crud.get_track_by_id(db, track_id)
+
+    if not track:
+        raise HTTPException(status_code=404, detail="Track not found")
+
+    file_path = Path(track.filepath)
+
+    if not file_path.exists():
+        logger.error(f"File not found: {track.filepath}")
+        raise HTTPException(status_code=404, detail="Audio file not found on disk")
+
+    try:
+        waveform_data = get_waveform_data(str(file_path), num_peaks=num_peaks)
+        return waveform_data
+
+    except Exception as e:
+        logger.error(f"Failed to generate waveform for {track_id}: {e}")
+        raise HTTPException(status_code=500, detail="Failed to generate waveform")
--- a/backend/src/api/routes/search.py
+++ b/backend/src/api/routes/search.py
@@ -0,0 +1,44 @@
+"""Search endpoints."""
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.orm import Session
+from typing import Optional
+
+from ...models.database import get_db
+from ...models import crud
+
+router = APIRouter()
+
+
+@router.get("")
+async def search_tracks(
+    q: str = Query(..., min_length=1, description="Search query"),
+    genre: Optional[str] = None,
+    mood: Optional[str] = None,
+    limit: int = Query(100, ge=1, le=500),
+    db: Session = Depends(get_db),
+):
+    """Search tracks by text query.
+
+    Args:
+        q: Search query string
+        genre: Optional genre filter
+        mood: Optional mood filter
+        limit: Maximum results
+        db: Database session
+
+    Returns:
+        List of matching tracks
+    """
+    tracks = crud.search_tracks(
+        db=db,
+        query=q,
+        genre=genre,
+        mood=mood,
+        limit=limit,
+    )
+
+    return {
+        "query": q,
+        "tracks": [track.to_dict() for track in tracks],
+        "total": len(tracks),
+    }
--- a/backend/src/api/routes/similar.py
+++ b/backend/src/api/routes/similar.py
@@ -0,0 +1,44 @@
+"""Similar tracks endpoints."""
+from fastapi import APIRouter, Depends, HTTPException, Query
+from sqlalchemy.orm import Session
+from uuid import UUID
+
+from ...models.database import get_db
+from ...models import crud
+
+router = APIRouter()
+
+
+@router.get("/tracks/{track_id}/similar")
+async def get_similar_tracks(
+    track_id: UUID,
+    limit: int = Query(10, ge=1, le=50),
+    db: Session = Depends(get_db),
+):
+    """Get tracks similar to the given track.
+
+    Args:
+        track_id: Reference track UUID
+        limit: Maximum results
+        db: Database session
+
+    Returns:
+        List of similar tracks
+
+    Raises:
+        HTTPException: 404 if track not found
+    """
+    # Check if reference track exists
+    ref_track = crud.get_track_by_id(db, track_id)
+
+    if not ref_track:
+        raise HTTPException(status_code=404, detail="Track not found")
+
+    # Get similar tracks
+    similar_tracks = crud.get_similar_tracks(db, track_id, limit=limit)
+
+    return {
+        "reference_track_id": str(track_id),
+        "similar_tracks": [track.to_dict() for track in similar_tracks],
+        "total": len(similar_tracks),
+    }
--- a/backend/src/api/routes/stats.py
+++ b/backend/src/api/routes/stats.py
@@ -0,0 +1,28 @@
+"""Statistics endpoints."""
+from fastapi import APIRouter, Depends
+from sqlalchemy.orm import Session
+
+from ...models.database import get_db
+from ...models import crud
+
+router = APIRouter()
+
+
+@router.get("")
+async def get_stats(db: Session = Depends(get_db)):
+    """Get database statistics.
+
+    Args:
+        db: Database session
+
+    Returns:
+        Statistics including:
+        - Total tracks
+        - Genre distribution
+        - Mood distribution
+        - Average BPM
+        - Total duration
+    """
+    stats = crud.get_stats(db)
+
+    return stats
--- a/backend/src/api/routes/tracks.py
+++ b/backend/src/api/routes/tracks.py
@@ -0,0 +1,118 @@
+"""Track management endpoints."""
+from fastapi import APIRouter, Depends, HTTPException, Query
+from sqlalchemy.orm import Session
+from typing import List, Optional
+from uuid import UUID
+
+from ...models.database import get_db
+from ...models import crud
+from ...models.schema import AudioTrack
+
+router = APIRouter()
+
+
+@router.get("", response_model=dict)
+async def get_tracks(
+    skip: int = Query(0, ge=0),
+    limit: int = Query(100, ge=1, le=500),
+    genre: Optional[str] = None,
+    mood: Optional[str] = None,
+    bpm_min: Optional[float] = Query(None, ge=0, le=300),
+    bpm_max: Optional[float] = Query(None, ge=0, le=300),
+    energy_min: Optional[float] = Query(None, ge=0, le=1),
+    energy_max: Optional[float] = Query(None, ge=0, le=1),
+    has_vocals: Optional[bool] = None,
+    sort_by: str = Query("analyzed_at", regex="^(analyzed_at|tempo_bpm|duration_seconds|filename|energy)$"),
+    sort_desc: bool = True,
+    db: Session = Depends(get_db),
+):
+    """Get tracks with filters and pagination.
+
+    Args:
+        skip: Number of records to skip
+        limit: Maximum number of records
+        genre: Filter by genre
+        mood: Filter by mood
+        bpm_min: Minimum BPM
+        bpm_max: Maximum BPM
+        energy_min: Minimum energy
+        energy_max: Maximum energy
+        has_vocals: Filter by vocal presence
+        sort_by: Field to sort by
+        sort_desc: Sort descending
+        db: Database session
+
+    Returns:
+        Paginated list of tracks with total count
+    """
+    tracks, total = crud.get_tracks(
+        db=db,
+        skip=skip,
+        limit=limit,
+        genre=genre,
+        mood=mood,
+        bpm_min=bpm_min,
+        bpm_max=bpm_max,
+        energy_min=energy_min,
+        energy_max=energy_max,
+        has_vocals=has_vocals,
+        sort_by=sort_by,
+        sort_desc=sort_desc,
+    )
+
+    return {
+        "tracks": [track.to_dict() for track in tracks],
+        "total": total,
+        "skip": skip,
+        "limit": limit,
+    }
+
+
+@router.get("/{track_id}")
+async def get_track(
+    track_id: UUID,
+    db: Session = Depends(get_db),
+):
+    """Get track by ID.
+
+    Args:
+        track_id: Track UUID
+        db: Database session
+
+    Returns:
+        Track details
+
+    Raises:
+        HTTPException: 404 if track not found
+    """
+    track = crud.get_track_by_id(db, track_id)
+
+    if not track:
+        raise HTTPException(status_code=404, detail="Track not found")
+
+    return track.to_dict()
+
+
+@router.delete("/{track_id}")
+async def delete_track(
+    track_id: UUID,
+    db: Session = Depends(get_db),
+):
+    """Delete track by ID.
+
+    Args:
+        track_id: Track UUID
+        db: Database session
+
+    Returns:
+        Success message
+
+    Raises:
+        HTTPException: 404 if track not found
+    """
+    success = crud.delete_track(db, track_id)
+
+    if not success:
+        raise HTTPException(status_code=404, detail="Track not found")
+
+    return {"message": "Track deleted successfully", "track_id": str(track_id)}
--- a/backend/src/core/init.py
+++ b/backend/src/core/init.py
--- a/backend/src/core/analyzer.py
+++ b/backend/src/core/analyzer.py
@@ -0,0 +1,222 @@
+"""Main audio analysis orchestrator."""
+from typing import Dict, List, Optional, Callable
+from pathlib import Path
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pydantic import BaseModel
+from datetime import datetime
+
+from .audio_processor import extract_all_features
+from .essentia_classifier import EssentiaClassifier
+from .file_scanner import get_file_metadata, scan_folder, validate_audio_files
+from ..utils.logging import get_logger
+from ..utils.config import settings
+
+logger = get_logger(__name__)
+
+
+class AudioAnalysis(BaseModel):
+    """Complete audio analysis result."""
+
+    # File info
+    filepath: str
+    filename: str
+    file_size_bytes: int
+    format: str
+    duration_seconds: Optional[float] = None
+    analyzed_at: datetime
+
+    # Audio features
+    tempo_bpm: Optional[float] = None
+    key: Optional[str] = None
+    time_signature: Optional[str] = None
+    energy: Optional[float] = None
+    danceability: Optional[float] = None
+    valence: Optional[float] = None
+    loudness_lufs: Optional[float] = None
+    spectral_centroid: Optional[float] = None
+    zero_crossing_rate: Optional[float] = None
+
+    # Classification
+    genre_primary: Optional[str] = None
+    genre_secondary: Optional[List[str]] = None
+    genre_confidence: Optional[float] = None
+    mood_primary: Optional[str] = None
+    mood_secondary: Optional[List[str]] = None
+    mood_arousal: Optional[float] = None
+    mood_valence: Optional[float] = None
+    instruments: Optional[List[str]] = None
+
+    # Vocals (future)
+    has_vocals: Optional[bool] = None
+    vocal_gender: Optional[str] = None
+
+    # Metadata
+    metadata: Optional[Dict] = None
+
+    class Config:
+        json_encoders = {
+            datetime: lambda v: v.isoformat()
+        }
+
+
+class AudioAnalyzer:
+    """Main audio analyzer orchestrating all processing steps."""
+
+    def __init__(self):
+        """Initialize analyzer with classifier."""
+        self.classifier = EssentiaClassifier()
+        self.num_workers = settings.ANALYSIS_NUM_WORKERS
+
+    def analyze_file(self, filepath: str) -> AudioAnalysis:
+        """Analyze a single audio file.
+
+        Args:
+            filepath: Path to audio file
+
+        Returns:
+            AudioAnalysis object with all extracted data
+
+        Raises:
+            Exception if analysis fails
+        """
+        logger.info(f"Analyzing file: {filepath}")
+
+        try:
+            # 1. Get file metadata
+            file_metadata = get_file_metadata(filepath)
+
+            # 2. Extract audio features (librosa)
+            audio_features = extract_all_features(filepath)
+
+            # 3. Classify with Essentia
+            genre = self.classifier.predict_genre(filepath)
+            mood = self.classifier.predict_mood(filepath)
+            instruments_list = self.classifier.predict_instruments(filepath)
+
+            # Extract instrument names only
+            instrument_names = [inst["name"] for inst in instruments_list]
+
+            # 4. Combine all data
+            analysis = AudioAnalysis(
+                # File info
+                filepath=file_metadata["filepath"],
+                filename=file_metadata["filename"],
+                file_size_bytes=file_metadata["file_size_bytes"],
+                format=file_metadata["format"],
+                duration_seconds=audio_features.get("duration_seconds"),
+                analyzed_at=datetime.utcnow(),
+
+                # Audio features
+                tempo_bpm=audio_features.get("tempo_bpm"),
+                key=audio_features.get("key"),
+                time_signature=audio_features.get("time_signature"),
+                energy=audio_features.get("energy"),
+                danceability=audio_features.get("danceability"),
+                valence=audio_features.get("valence"),
+                loudness_lufs=audio_features.get("loudness_lufs"),
+                spectral_centroid=audio_features.get("spectral_centroid"),
+                zero_crossing_rate=audio_features.get("zero_crossing_rate"),
+
+                # Classification
+                genre_primary=genre.get("primary"),
+                genre_secondary=genre.get("secondary"),
+                genre_confidence=genre.get("confidence"),
+                mood_primary=mood.get("primary"),
+                mood_secondary=mood.get("secondary"),
+                mood_arousal=mood.get("arousal"),
+                mood_valence=mood.get("valence"),
+                instruments=instrument_names,
+
+                # Metadata
+                metadata=file_metadata.get("id3_tags"),
+            )
+
+            logger.info(f"Successfully analyzed: {filepath}")
+            return analysis
+
+        except Exception as e:
+            logger.error(f"Failed to analyze {filepath}: {e}")
+            raise
+
+    def analyze_folder(
+        self,
+        path: str,
+        recursive: bool = True,
+        progress_callback: Optional[Callable[[int, int, str], None]] = None,
+    ) -> List[AudioAnalysis]:
+        """Analyze all audio files in a folder.
+
+        Args:
+            path: Directory path
+            recursive: If True, scan recursively
+            progress_callback: Optional callback(current, total, filename)
+
+        Returns:
+            List of AudioAnalysis objects
+        """
+        logger.info(f"Analyzing folder: {path}")
+
+        # 1. Scan for files
+        audio_files = scan_folder(path, recursive=recursive)
+        total_files = len(audio_files)
+
+        if total_files == 0:
+            logger.warning(f"No audio files found in {path}")
+            return []
+
+        logger.info(f"Found {total_files} files to analyze")
+
+        # 2. Analyze files in parallel
+        results = []
+        errors = []
+
+        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
+            # Submit all tasks
+            future_to_file = {
+                executor.submit(self._analyze_file_safe, filepath): filepath
+                for filepath in audio_files
+            }
+
+            # Process completed tasks
+            for i, future in enumerate(as_completed(future_to_file), 1):
+                filepath = future_to_file[future]
+                filename = Path(filepath).name
+
+                # Call progress callback
+                if progress_callback:
+                    progress_callback(i, total_files, filename)
+
+                try:
+                    analysis = future.result()
+                    if analysis:
+                        results.append(analysis)
+                        logger.info(f"[{i}/{total_files}] ✓ {filename}")
+                    else:
+                        errors.append(filepath)
+                        logger.warning(f"[{i}/{total_files}] ✗ {filename}")
+
+                except Exception as e:
+                    errors.append(filepath)
+                    logger.error(f"[{i}/{total_files}] ✗ {filename}: {e}")
+
+        logger.info(f"Analysis complete: {len(results)} succeeded, {len(errors)} failed")
+
+        if errors:
+            logger.warning(f"Failed files: {errors[:10]}")  # Log first 10
+
+        return results
+
+    def _analyze_file_safe(self, filepath: str) -> Optional[AudioAnalysis]:
+        """Safely analyze a file (catches exceptions).
+
+        Args:
+            filepath: Path to audio file
+
+        Returns:
+            AudioAnalysis or None if failed
+        """
+        try:
+            return self.analyze_file(filepath)
+        except Exception as e:
+            logger.error(f"Analysis failed for {filepath}: {e}")
+            return None
--- a/backend/src/core/audio_processor.py
+++ b/backend/src/core/audio_processor.py
@@ -0,0 +1,342 @@
+"""Audio feature extraction using librosa."""
+import librosa
+import numpy as np
+from typing import Dict, Tuple, Optional
+import warnings
+
+from ..utils.logging import get_logger
+
+logger = get_logger(__name__)
+
+# Suppress librosa warnings
+warnings.filterwarnings('ignore', category=UserWarning, module='librosa')
+
+
+def load_audio(filepath: str, sr: int = 22050) -> Tuple[np.ndarray, int]:
+    """Load audio file.
+
+    Args:
+        filepath: Path to audio file
+        sr: Target sample rate (default: 22050 Hz)
+
+    Returns:
+        Tuple of (audio time series, sample rate)
+    """
+    try:
+        y, sr = librosa.load(filepath, sr=sr, mono=True)
+        return y, sr
+    except Exception as e:
+        logger.error(f"Failed to load audio file {filepath}: {e}")
+        raise
+
+
+def extract_tempo(y: np.ndarray, sr: int) -> float:
+    """Extract tempo (BPM) from audio.
+
+    Args:
+        y: Audio time series
+        sr: Sample rate
+
+    Returns:
+        Tempo in BPM
+    """
+    try:
+        # Use onset_envelope for better beat tracking
+        onset_env = librosa.onset.onset_strength(y=y, sr=sr)
+        tempo, _ = librosa.beat.beat_track(onset_envelope=onset_env, sr=sr)
+        return float(tempo)
+    except Exception as e:
+        logger.warning(f"Failed to extract tempo: {e}")
+        return 0.0
+
+
+def extract_key(y: np.ndarray, sr: int) -> str:
+    """Extract musical key from audio.
+
+    Args:
+        y: Audio time series
+        sr: Sample rate
+
+    Returns:
+        Key as string (e.g., "C major", "D minor")
+    """
+    try:
+        # Extract chroma features
+        chromagram = librosa.feature.chroma_cqt(y=y, sr=sr)
+
+        # Average chroma across time
+        chroma_mean = np.mean(chromagram, axis=1)
+
+        # Find dominant pitch class
+        key_idx = np.argmax(chroma_mean)
+
+        # Map to note names
+        notes = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
+
+        # Simple major/minor detection (can be improved)
+        # Check if minor third is prominent
+        minor_third_idx = (key_idx + 3) % 12
+        is_minor = chroma_mean[minor_third_idx] > chroma_mean.mean()
+
+        mode = "minor" if is_minor else "major"
+        return f"{notes[key_idx]} {mode}"
+
+    except Exception as e:
+        logger.warning(f"Failed to extract key: {e}")
+        return "unknown"
+
+
+def extract_spectral_features(y: np.ndarray, sr: int) -> Dict[str, float]:
+    """Extract spectral features.
+
+    Args:
+        y: Audio time series
+        sr: Sample rate
+
+    Returns:
+        Dictionary with spectral features
+    """
+    try:
+        # Spectral centroid
+        spectral_centroids = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
+        spectral_centroid_mean = float(np.mean(spectral_centroids))
+
+        # Zero crossing rate
+        zcr = librosa.feature.zero_crossing_rate(y)[0]
+        zcr_mean = float(np.mean(zcr))
+
+        # Spectral rolloff
+        spectral_rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)[0]
+        spectral_rolloff_mean = float(np.mean(spectral_rolloff))
+
+        # Spectral bandwidth
+        spectral_bandwidth = librosa.feature.spectral_bandwidth(y=y, sr=sr)[0]
+        spectral_bandwidth_mean = float(np.mean(spectral_bandwidth))
+
+        return {
+            "spectral_centroid": spectral_centroid_mean,
+            "zero_crossing_rate": zcr_mean,
+            "spectral_rolloff": spectral_rolloff_mean,
+            "spectral_bandwidth": spectral_bandwidth_mean,
+        }
+
+    except Exception as e:
+        logger.warning(f"Failed to extract spectral features: {e}")
+        return {
+            "spectral_centroid": 0.0,
+            "zero_crossing_rate": 0.0,
+            "spectral_rolloff": 0.0,
+            "spectral_bandwidth": 0.0,
+        }
+
+
+def extract_energy(y: np.ndarray, sr: int) -> float:
+    """Extract RMS energy.
+
+    Args:
+        y: Audio time series
+        sr: Sample rate
+
+    Returns:
+        Normalized energy value (0-1)
+    """
+    try:
+        rms = librosa.feature.rms(y=y)[0]
+        energy = float(np.mean(rms))
+        # Normalize to 0-1 range (approximate)
+        return min(energy * 10, 1.0)
+    except Exception as e:
+        logger.warning(f"Failed to extract energy: {e}")
+        return 0.0
+
+
+def estimate_danceability(y: np.ndarray, sr: int, tempo: float) -> float:
+    """Estimate danceability based on rhythm and tempo.
+
+    Args:
+        y: Audio time series
+        sr: Sample rate
+        tempo: BPM
+
+    Returns:
+        Danceability score (0-1)
+    """
+    try:
+        # Danceability is correlated with:
+        # 1. Strong beat regularity
+        # 2. Tempo in danceable range (90-150 BPM)
+        # 3. Percussive content
+
+        # Get onset strength
+        onset_env = librosa.onset.onset_strength(y=y, sr=sr)
+
+        # Calculate beat regularity (autocorrelation of onset strength)
+        ac = librosa.autocorrelate(onset_env, max_size=sr // 512)
+        ac_peak = float(np.max(ac[1:]) / (ac[0] + 1e-8))  # Normalize by first value
+
+        # Tempo factor (optimal around 90-150 BPM)
+        if 90 <= tempo <= 150:
+            tempo_factor = 1.0
+        elif 70 <= tempo < 90 or 150 < tempo <= 180:
+            tempo_factor = 0.7
+        else:
+            tempo_factor = 0.4
+
+        # Combine factors
+        danceability = min(ac_peak * tempo_factor, 1.0)
+        return float(danceability)
+
+    except Exception as e:
+        logger.warning(f"Failed to estimate danceability: {e}")
+        return 0.0
+
+
+def estimate_valence(y: np.ndarray, sr: int) -> float:
+    """Estimate valence (positivity) based on audio features.
+
+    Args:
+        y: Audio time series
+        sr: Sample rate
+
+    Returns:
+        Valence score (0-1), where 1 is positive/happy
+    """
+    try:
+        # Valence is correlated with:
+        # 1. Major key vs minor key
+        # 2. Higher tempo
+        # 3. Brighter timbre (higher spectral centroid)
+
+        # Get chroma for major/minor detection
+        chromagram = librosa.feature.chroma_cqt(y=y, sr=sr)
+        chroma_mean = np.mean(chromagram, axis=1)
+
+        # Get spectral centroid (brightness)
+        spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
+        brightness = float(np.mean(spectral_centroid) / (sr / 2))  # Normalize
+
+        # Simple heuristic: combine brightness with mode
+        # Higher spectral centroid = more positive
+        valence = min(brightness * 1.5, 1.0)
+
+        return float(valence)
+
+    except Exception as e:
+        logger.warning(f"Failed to estimate valence: {e}")
+        return 0.5  # Neutral
+
+
+def estimate_loudness(y: np.ndarray, sr: int) -> float:
+    """Estimate loudness in LUFS (approximate).
+
+    Args:
+        y: Audio time series
+        sr: Sample rate
+
+    Returns:
+        Approximate loudness in LUFS
+    """
+    try:
+        # This is a simplified estimation
+        # True LUFS requires ITU-R BS.1770 weighting
+        rms = np.sqrt(np.mean(y**2))
+
+        # Convert to dB
+        db = 20 * np.log10(rms + 1e-10)
+
+        # Approximate LUFS (very rough estimate)
+        lufs = db + 0.691  # Offset to approximate LUFS
+
+        return float(lufs)
+
+    except Exception as e:
+        logger.warning(f"Failed to estimate loudness: {e}")
+        return -14.0  # Default target loudness
+
+
+def extract_time_signature(y: np.ndarray, sr: int) -> str:
+    """Estimate time signature.
+
+    Args:
+        y: Audio time series
+        sr: Sample rate
+
+    Returns:
+        Time signature as string (e.g., "4/4", "3/4")
+
+    Note:
+        This is a simplified estimation. Accurate time signature detection
+        is complex and often requires machine learning models.
+    """
+    try:
+        # Get tempo and beat frames
+        onset_env = librosa.onset.onset_strength(y=y, sr=sr)
+        tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env, sr=sr)
+
+        # Analyze beat intervals
+        if len(beats) < 4:
+            return "4/4"  # Default
+
+        beat_times = librosa.frames_to_time(beats, sr=sr)
+        intervals = np.diff(beat_times)
+
+        # Look for patterns (very simplified)
+        # This is placeholder logic - real implementation would be much more complex
+        return "4/4"  # Default to 4/4 for now
+
+    except Exception as e:
+        logger.warning(f"Failed to extract time signature: {e}")
+        return "4/4"
+
+
+def extract_all_features(filepath: str) -> Dict:
+    """Extract all audio features from a file.
+
+    Args:
+        filepath: Path to audio file
+
+    Returns:
+        Dictionary with all extracted features
+    """
+    logger.info(f"Extracting features from: {filepath}")
+
+    try:
+        # Load audio
+        y, sr = load_audio(filepath)
+
+        # Get duration
+        duration = float(librosa.get_duration(y=y, sr=sr))
+
+        # Extract tempo first (used by other features)
+        tempo = extract_tempo(y, sr)
+
+        # Extract all features
+        key = extract_key(y, sr)
+        spectral_features = extract_spectral_features(y, sr)
+        energy = extract_energy(y, sr)
+        danceability = estimate_danceability(y, sr, tempo)
+        valence = estimate_valence(y, sr)
+        loudness = estimate_loudness(y, sr)
+        time_signature = extract_time_signature(y, sr)
+
+        features = {
+            "duration_seconds": duration,
+            "tempo_bpm": tempo,
+            "key": key,
+            "time_signature": time_signature,
+            "energy": energy,
+            "danceability": danceability,
+            "valence": valence,
+            "loudness_lufs": loudness,
+            "spectral_centroid": spectral_features["spectral_centroid"],
+            "zero_crossing_rate": spectral_features["zero_crossing_rate"],
+            "spectral_rolloff": spectral_features["spectral_rolloff"],
+            "spectral_bandwidth": spectral_features["spectral_bandwidth"],
+        }
+
+        logger.info(f"Successfully extracted features: tempo={tempo:.1f} BPM, key={key}")
+        return features
+
+    except Exception as e:
+        logger.error(f"Failed to extract features from {filepath}: {e}")
+        raise
--- a/backend/src/core/essentia_classifier.py
+++ b/backend/src/core/essentia_classifier.py
@@ -0,0 +1,300 @@
+"""Music classification using Essentia-TensorFlow models."""
+import os
+from pathlib import Path
+from typing import Dict, List, Optional
+import numpy as np
+
+from ..utils.logging import get_logger
+from ..utils.config import settings
+
+logger = get_logger(__name__)
+
+# Try to import essentia
+try:
+    from essentia.standard import (
+        MonoLoader,
+        TensorflowPredictEffnetDiscogs,
+        TensorflowPredict2D
+    )
+    ESSENTIA_AVAILABLE = True
+except ImportError:
+    logger.warning("Essentia-TensorFlow not available. Classification will be limited.")
+    ESSENTIA_AVAILABLE = False
+
+
+class EssentiaClassifier:
+    """Classifier using Essentia pre-trained models."""
+
+    # Model URLs (for documentation)
+    MODEL_URLS = {
+        "genre": "https://essentia.upf.edu/models/classification-heads/mtg_jamendo_genre/mtg_jamendo_genre-discogs-effnet-1.pb",
+        "mood": "https://essentia.upf.edu/models/classification-heads/mtg_jamendo_moodtheme/mtg_jamendo_moodtheme-discogs-effnet-1.pb",
+        "instrument": "https://essentia.upf.edu/models/classification-heads/mtg_jamendo_instrument/mtg_jamendo_instrument-discogs-effnet-1.pb",
+    }
+
+    def __init__(self, models_path: Optional[str] = None):
+        """Initialize Essentia classifier.
+
+        Args:
+            models_path: Path to models directory (default: from settings)
+        """
+        self.models_path = Path(models_path or settings.ESSENTIA_MODELS_PATH)
+        self.models = {}
+        self.class_labels = {}
+
+        if not ESSENTIA_AVAILABLE:
+            logger.warning("Essentia not available - using fallback classifications")
+            return
+
+        # Load models if available
+        self._load_models()
+
+    def _load_models(self) -> None:
+        """Load Essentia TensorFlow models."""
+        if not self.models_path.exists():
+            logger.warning(f"Models path {self.models_path} does not exist")
+            return
+
+        # Model file names
+        model_files = {
+            "genre": "mtg_jamendo_genre-discogs-effnet-1.pb",
+            "mood": "mtg_jamendo_moodtheme-discogs-effnet-1.pb",
+            "instrument": "mtg_jamendo_instrument-discogs-effnet-1.pb",
+        }
+
+        for model_name, model_file in model_files.items():
+            model_path = self.models_path / model_file
+            if model_path.exists():
+                try:
+                    logger.info(f"Loading {model_name} model from {model_path}")
+                    # Models will be loaded on demand
+                    self.models[model_name] = str(model_path)
+                except Exception as e:
+                    logger.error(f"Failed to load {model_name} model: {e}")
+            else:
+                logger.warning(f"Model file not found: {model_path}")
+
+        # Load class labels
+        self._load_class_labels()
+
+    def _load_class_labels(self) -> None:
+        """Load class labels for models."""
+        # These are the actual class labels from MTG-Jamendo dataset
+        # In production, these should be loaded from JSON files
+
+        self.class_labels["genre"] = [
+            "rock", "pop", "alternative", "indie", "electronic",
+            "female vocalists", "dance", "00s", "alternative rock", "jazz",
+            "beautiful", "metal", "chillout", "male vocalists", "classic rock",
+            "soul", "indie rock", "Mellow", "electronica", "80s",
+            "folk", "90s", "chill", "instrumental", "punk",
+            "oldies", "blues", "hard rock", "ambient", "acoustic",
+            "experimental", "female vocalist", "guitar", "Hip-Hop", "70s",
+            "party", "country", "easy listening", "sexy", "catchy",
+            "funk", "electro", "heavy metal", "Progressive rock", "60s",
+            "rnb", "indie pop", "sad", "House", "happy"
+        ]
+
+        self.class_labels["mood"] = [
+            "action", "adventure", "advertising", "background", "ballad",
+            "calm", "children", "christmas", "commercial", "cool",
+            "corporate", "dark", "deep", "documentary", "drama",
+            "dramatic", "dream", "emotional", "energetic", "epic",
+            "fast", "film", "fun", "funny", "game",
+            "groovy", "happy", "heavy", "holiday", "hopeful",
+            "inspiring", "love", "meditative", "melancholic", "mellow",
+            "melodic", "motivational", "movie", "nature", "party",
+            "positive", "powerful", "relaxing", "retro", "romantic",
+            "sad", "sexy", "slow", "soft", "soundscape",
+            "space", "sport", "summer", "trailer", "travel",
+            "upbeat", "uplifting"
+        ]
+
+        self.class_labels["instrument"] = [
+            "accordion", "acousticbassguitar", "acousticguitar", "bass",
+            "beat", "bell", "bongo", "brass", "cello",
+            "clarinet", "classicalguitar", "computer", "doublebass", "drummachine",
+            "drums", "electricguitar", "electricpiano", "flute", "guitar",
+            "harmonica", "harp", "horn", "keyboard", "oboe",
+            "orchestra", "organ", "pad", "percussion", "piano",
+            "pipeorgan", "rhodes", "sampler", "saxophone", "strings",
+            "synthesizer", "trombone", "trumpet", "viola", "violin",
+            "voice"
+        ]
+
+    def predict_genre(self, audio_path: str) -> Dict:
+        """Predict music genre.
+
+        Args:
+            audio_path: Path to audio file
+
+        Returns:
+            Dictionary with genre predictions
+        """
+        if not ESSENTIA_AVAILABLE or "genre" not in self.models:
+            return self._fallback_genre()
+
+        try:
+            # Load audio
+            audio = MonoLoader(filename=audio_path, sampleRate=16000, resampleQuality=4)()
+
+            # Predict
+            model = TensorflowPredictEffnetDiscogs(
+                graphFilename=self.models["genre"],
+                output="PartitionedCall:1"
+            )
+            predictions = model(audio)
+
+            # Get top predictions
+            top_indices = np.argsort(predictions)[::-1][:5]
+            labels = self.class_labels.get("genre", [])
+
+            primary = labels[top_indices[0]] if labels else "unknown"
+            secondary = [labels[i] for i in top_indices[1:4]] if labels else []
+            confidence = float(predictions[top_indices[0]])
+
+            return {
+                "primary": primary,
+                "secondary": secondary,
+                "confidence": confidence,
+            }
+
+        except Exception as e:
+            logger.error(f"Genre prediction failed: {e}")
+            return self._fallback_genre()
+
+    def predict_mood(self, audio_path: str) -> Dict:
+        """Predict mood/theme.
+
+        Args:
+            audio_path: Path to audio file
+
+        Returns:
+            Dictionary with mood predictions
+        """
+        if not ESSENTIA_AVAILABLE or "mood" not in self.models:
+            return self._fallback_mood()
+
+        try:
+            # Load audio
+            audio = MonoLoader(filename=audio_path, sampleRate=16000, resampleQuality=4)()
+
+            # Predict
+            model = TensorflowPredictEffnetDiscogs(
+                graphFilename=self.models["mood"],
+                output="PartitionedCall:1"
+            )
+            predictions = model(audio)
+
+            # Get top predictions
+            top_indices = np.argsort(predictions)[::-1][:5]
+            labels = self.class_labels.get("mood", [])
+
+            primary = labels[top_indices[0]] if labels else "unknown"
+            secondary = [labels[i] for i in top_indices[1:3]] if labels else []
+
+            # Estimate arousal and valence from mood labels (simplified)
+            arousal, valence = self._estimate_arousal_valence(primary)
+
+            return {
+                "primary": primary,
+                "secondary": secondary,
+                "arousal": arousal,
+                "valence": valence,
+            }
+
+        except Exception as e:
+            logger.error(f"Mood prediction failed: {e}")
+            return self._fallback_mood()
+
+    def predict_instruments(self, audio_path: str) -> List[Dict]:
+        """Predict instruments.
+
+        Args:
+            audio_path: Path to audio file
+
+        Returns:
+            List of instruments with confidence scores
+        """
+        if not ESSENTIA_AVAILABLE or "instrument" not in self.models:
+            return self._fallback_instruments()
+
+        try:
+            # Load audio
+            audio = MonoLoader(filename=audio_path, sampleRate=16000, resampleQuality=4)()
+
+            # Predict
+            model = TensorflowPredictEffnetDiscogs(
+                graphFilename=self.models["instrument"],
+                output="PartitionedCall:1"
+            )
+            predictions = model(audio)
+
+            # Get instruments above threshold
+            threshold = 0.1
+            labels = self.class_labels.get("instrument", [])
+            instruments = []
+
+            for i, score in enumerate(predictions):
+                if score > threshold and i < len(labels):
+                    instruments.append({
+                        "name": labels[i],
+                        "confidence": float(score)
+                    })
+
+            # Sort by confidence
+            instruments.sort(key=lambda x: x["confidence"], reverse=True)
+
+            return instruments[:10]  # Top 10
+
+        except Exception as e:
+            logger.error(f"Instrument prediction failed: {e}")
+            return self._fallback_instruments()
+
+    def _estimate_arousal_valence(self, mood: str) -> tuple:
+        """Estimate arousal and valence from mood label.
+
+        Args:
+            mood: Mood label
+
+        Returns:
+            Tuple of (arousal, valence) scores (0-1)
+        """
+        # Simplified mapping (in production, use trained model)
+        arousal_map = {
+            "energetic": 0.9, "powerful": 0.9, "fast": 0.9, "action": 0.9,
+            "calm": 0.2, "relaxing": 0.2, "meditative": 0.1, "slow": 0.3,
+            "upbeat": 0.8, "party": 0.9, "groovy": 0.7,
+        }
+
+        valence_map = {
+            "happy": 0.9, "positive": 0.9, "uplifting": 0.9, "fun": 0.9,
+            "sad": 0.1, "dark": 0.2, "melancholic": 0.2, "dramatic": 0.3,
+            "energetic": 0.7, "calm": 0.6, "romantic": 0.7,
+        }
+
+        arousal = arousal_map.get(mood.lower(), 0.5)
+        valence = valence_map.get(mood.lower(), 0.5)
+
+        return arousal, valence
+
+    def _fallback_genre(self) -> Dict:
+        """Fallback genre when model not available."""
+        return {
+            "primary": "unknown",
+            "secondary": [],
+            "confidence": 0.0,
+        }
+
+    def _fallback_mood(self) -> Dict:
+        """Fallback mood when model not available."""
+        return {
+            "primary": "unknown",
+            "secondary": [],
+            "arousal": 0.5,
+            "valence": 0.5,
+        }
+
+    def _fallback_instruments(self) -> List[Dict]:
+        """Fallback instruments when model not available."""
+        return []
--- a/backend/src/core/file_scanner.py
+++ b/backend/src/core/file_scanner.py
@@ -0,0 +1,111 @@
+"""File scanning and metadata extraction."""
+import os
+from pathlib import Path
+from typing import List, Dict, Optional
+from mutagen import File as MutagenFile
+
+from ..utils.logging import get_logger
+from ..utils.validators import get_audio_files, is_audio_file
+
+logger = get_logger(__name__)
+
+
+def scan_folder(path: str, recursive: bool = True) -> List[str]:
+    """Scan folder for audio files.
+
+    Args:
+        path: Directory path to scan
+        recursive: If True, scan subdirectories recursively
+
+    Returns:
+        List of absolute paths to audio files
+    """
+    logger.info(f"Scanning folder: {path} (recursive={recursive})")
+
+    try:
+        audio_files = get_audio_files(path, recursive=recursive)
+        logger.info(f"Found {len(audio_files)} audio files")
+        return audio_files
+
+    except Exception as e:
+        logger.error(f"Failed to scan folder {path}: {e}")
+        return []
+
+
+def get_file_metadata(filepath: str) -> Dict:
+    """Get file metadata including ID3 tags.
+
+    Args:
+        filepath: Path to audio file
+
+    Returns:
+        Dictionary with file metadata
+    """
+    try:
+        file_path = Path(filepath)
+
+        # Basic file info
+        metadata = {
+            "filename": file_path.name,
+            "file_size_bytes": file_path.stat().st_size,
+            "format": file_path.suffix.lstrip('.').lower(),
+            "filepath": str(file_path.resolve()),
+        }
+
+        # Try to get ID3 tags
+        try:
+            audio_file = MutagenFile(filepath, easy=True)
+            if audio_file is not None:
+                # Extract common tags
+                tags = {}
+                if hasattr(audio_file, 'tags') and audio_file.tags:
+                    for key in ['title', 'artist', 'album', 'genre', 'date']:
+                        if key in audio_file.tags:
+                            value = audio_file.tags[key]
+                            tags[key] = value[0] if isinstance(value, list) else str(value)
+
+                if tags:
+                    metadata["id3_tags"] = tags
+
+                # Get duration from mutagen if available
+                if hasattr(audio_file, 'info') and hasattr(audio_file.info, 'length'):
+                    metadata["duration_seconds"] = float(audio_file.info.length)
+
+        except Exception as e:
+            logger.debug(f"Could not read tags from {filepath}: {e}")
+
+        return metadata
+
+    except Exception as e:
+        logger.error(f"Failed to get metadata for {filepath}: {e}")
+        return {
+            "filename": Path(filepath).name,
+            "file_size_bytes": 0,
+            "format": "unknown",
+            "filepath": filepath,
+        }
+
+
+def validate_audio_files(filepaths: List[str]) -> List[str]:
+    """Validate a list of file paths and return only valid audio files.
+
+    Args:
+        filepaths: List of file paths to validate
+
+    Returns:
+        List of valid audio file paths
+    """
+    valid_files = []
+
+    for filepath in filepaths:
+        if not Path(filepath).exists():
+            logger.warning(f"File does not exist: {filepath}")
+            continue
+
+        if not is_audio_file(filepath):
+            logger.warning(f"Not a supported audio file: {filepath}")
+            continue
+
+        valid_files.append(filepath)
+
+    return valid_files
--- a/backend/src/core/waveform_generator.py
+++ b/backend/src/core/waveform_generator.py
@@ -0,0 +1,119 @@
+"""Waveform peak generation for visualization."""
+import librosa
+import numpy as np
+from pathlib import Path
+from typing import List, Optional
+import json
+
+from ..utils.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+def generate_peaks(filepath: str, num_peaks: int = 800, use_cache: bool = True) -> List[float]:
+    """Generate waveform peaks for visualization.
+
+    Args:
+        filepath: Path to audio file
+        num_peaks: Number of peaks to generate (default: 800)
+        use_cache: Whether to use cached peaks if available
+
+    Returns:
+        List of normalized peak values (0-1)
+    """
+    cache_file = Path(filepath).with_suffix('.peaks.json')
+
+    # Try to load from cache
+    if use_cache and cache_file.exists():
+        try:
+            with open(cache_file, 'r') as f:
+                cached_data = json.load(f)
+                if cached_data.get('num_peaks') == num_peaks:
+                    logger.debug(f"Loading peaks from cache: {cache_file}")
+                    return cached_data['peaks']
+        except Exception as e:
+            logger.warning(f"Failed to load cached peaks: {e}")
+
+    try:
+        logger.debug(f"Generating {num_peaks} peaks for {filepath}")
+
+        # Load audio
+        y, sr = librosa.load(filepath, sr=None, mono=True)
+
+        # Calculate how many samples per peak
+        total_samples = len(y)
+        samples_per_peak = max(1, total_samples // num_peaks)
+
+        peaks = []
+        for i in range(num_peaks):
+            start_idx = i * samples_per_peak
+            end_idx = min(start_idx + samples_per_peak, total_samples)
+
+            if start_idx >= total_samples:
+                peaks.append(0.0)
+                continue
+
+            # Get chunk
+            chunk = y[start_idx:end_idx]
+
+            # Calculate peak (max absolute value)
+            peak = float(np.max(np.abs(chunk))) if len(chunk) > 0 else 0.0
+            peaks.append(peak)
+
+        # Normalize peaks to 0-1 range
+        max_peak = max(peaks) if peaks else 1.0
+        if max_peak > 0:
+            peaks = [p / max_peak for p in peaks]
+
+        # Cache the peaks
+        if use_cache:
+            try:
+                cache_data = {
+                    'num_peaks': num_peaks,
+                    'peaks': peaks,
+                    'duration': float(librosa.get_duration(y=y, sr=sr))
+                }
+                with open(cache_file, 'w') as f:
+                    json.dump(cache_data, f)
+                logger.debug(f"Cached peaks to {cache_file}")
+            except Exception as e:
+                logger.warning(f"Failed to cache peaks: {e}")
+
+        return peaks
+
+    except Exception as e:
+        logger.error(f"Failed to generate peaks for {filepath}: {e}")
+        # Return empty peaks
+        return [0.0] * num_peaks
+
+
+def get_waveform_data(filepath: str, num_peaks: int = 800) -> dict:
+    """Get complete waveform data including peaks and duration.
+
+    Args:
+        filepath: Path to audio file
+        num_peaks: Number of peaks
+
+    Returns:
+        Dictionary with peaks and duration
+    """
+    try:
+        peaks = generate_peaks(filepath, num_peaks)
+
+        # Get duration
+        y, sr = librosa.load(filepath, sr=None, mono=True)
+        duration = float(librosa.get_duration(y=y, sr=sr))
+
+        return {
+            'peaks': peaks,
+            'duration': duration,
+            'num_peaks': num_peaks
+        }
+
+    except Exception as e:
+        logger.error(f"Failed to get waveform data: {e}")
+        return {
+            'peaks': [0.0] * num_peaks,
+            'duration': 0.0,
+            'num_peaks': num_peaks
+        }
--- a/backend/src/models/init.py
+++ b/backend/src/models/init.py
--- a/backend/src/models/crud.py
+++ b/backend/src/models/crud.py
@@ -0,0 +1,390 @@
+"""CRUD operations for audio tracks."""
+from typing import List, Optional, Dict
+from uuid import UUID
+from sqlalchemy.orm import Session
+from sqlalchemy import or_, and_, func
+
+from .schema import AudioTrack
+from ..core.analyzer import AudioAnalysis
+from ..utils.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+def create_track(db: Session, analysis: AudioAnalysis) -> AudioTrack:
+    """Create a new track from analysis data.
+
+    Args:
+        db: Database session
+        analysis: AudioAnalysis object
+
+    Returns:
+        Created AudioTrack instance
+    """
+    track = AudioTrack(
+        filepath=analysis.filepath,
+        filename=analysis.filename,
+        duration_seconds=analysis.duration_seconds,
+        file_size_bytes=analysis.file_size_bytes,
+        format=analysis.format,
+        analyzed_at=analysis.analyzed_at,
+
+        # Features
+        tempo_bpm=analysis.tempo_bpm,
+        key=analysis.key,
+        time_signature=analysis.time_signature,
+        energy=analysis.energy,
+        danceability=analysis.danceability,
+        valence=analysis.valence,
+        loudness_lufs=analysis.loudness_lufs,
+        spectral_centroid=analysis.spectral_centroid,
+        zero_crossing_rate=analysis.zero_crossing_rate,
+
+        # Classification
+        genre_primary=analysis.genre_primary,
+        genre_secondary=analysis.genre_secondary,
+        genre_confidence=analysis.genre_confidence,
+        mood_primary=analysis.mood_primary,
+        mood_secondary=analysis.mood_secondary,
+        mood_arousal=analysis.mood_arousal,
+        mood_valence=analysis.mood_valence,
+        instruments=analysis.instruments,
+
+        # Vocals
+        has_vocals=analysis.has_vocals,
+        vocal_gender=analysis.vocal_gender,
+
+        # Metadata
+        metadata=analysis.metadata,
+    )
+
+    db.add(track)
+    db.commit()
+    db.refresh(track)
+
+    logger.info(f"Created track: {track.id} - {track.filename}")
+    return track
+
+
+def get_track_by_id(db: Session, track_id: UUID) -> Optional[AudioTrack]:
+    """Get track by ID.
+
+    Args:
+        db: Database session
+        track_id: Track UUID
+
+    Returns:
+        AudioTrack or None if not found
+    """
+    return db.query(AudioTrack).filter(AudioTrack.id == track_id).first()
+
+
+def get_track_by_filepath(db: Session, filepath: str) -> Optional[AudioTrack]:
+    """Get track by filepath.
+
+    Args:
+        db: Database session
+        filepath: File path
+
+    Returns:
+        AudioTrack or None if not found
+    """
+    return db.query(AudioTrack).filter(AudioTrack.filepath == filepath).first()
+
+
+def get_tracks(
+    db: Session,
+    skip: int = 0,
+    limit: int = 100,
+    genre: Optional[str] = None,
+    mood: Optional[str] = None,
+    bpm_min: Optional[float] = None,
+    bpm_max: Optional[float] = None,
+    energy_min: Optional[float] = None,
+    energy_max: Optional[float] = None,
+    has_vocals: Optional[bool] = None,
+    sort_by: str = "analyzed_at",
+    sort_desc: bool = True,
+) -> tuple[List[AudioTrack], int]:
+    """Get tracks with filters and pagination.
+
+    Args:
+        db: Database session
+        skip: Number of records to skip
+        limit: Maximum number of records to return
+        genre: Filter by genre
+        mood: Filter by mood
+        bpm_min: Minimum BPM
+        bpm_max: Maximum BPM
+        energy_min: Minimum energy (0-1)
+        energy_max: Maximum energy (0-1)
+        has_vocals: Filter by vocal presence
+        sort_by: Field to sort by
+        sort_desc: Sort descending if True
+
+    Returns:
+        Tuple of (tracks list, total count)
+    """
+    query = db.query(AudioTrack)
+
+    # Apply filters
+    if genre:
+        query = query.filter(
+            or_(
+                AudioTrack.genre_primary == genre,
+                AudioTrack.genre_secondary.contains([genre])
+            )
+        )
+
+    if mood:
+        query = query.filter(
+            or_(
+                AudioTrack.mood_primary == mood,
+                AudioTrack.mood_secondary.contains([mood])
+            )
+        )
+
+    if bpm_min is not None:
+        query = query.filter(AudioTrack.tempo_bpm >= bpm_min)
+
+    if bpm_max is not None:
+        query = query.filter(AudioTrack.tempo_bpm <= bpm_max)
+
+    if energy_min is not None:
+        query = query.filter(AudioTrack.energy >= energy_min)
+
+    if energy_max is not None:
+        query = query.filter(AudioTrack.energy <= energy_max)
+
+    if has_vocals is not None:
+        query = query.filter(AudioTrack.has_vocals == has_vocals)
+
+    # Get total count before pagination
+    total = query.count()
+
+    # Apply sorting
+    if hasattr(AudioTrack, sort_by):
+        sort_column = getattr(AudioTrack, sort_by)
+        if sort_desc:
+            query = query.order_by(sort_column.desc())
+        else:
+            query = query.order_by(sort_column.asc())
+
+    # Apply pagination
+    tracks = query.offset(skip).limit(limit).all()
+
+    return tracks, total
+
+
+def search_tracks(
+    db: Session,
+    query: str,
+    genre: Optional[str] = None,
+    mood: Optional[str] = None,
+    limit: int = 100,
+) -> List[AudioTrack]:
+    """Search tracks by text query.
+
+    Args:
+        db: Database session
+        query: Search query string
+        genre: Optional genre filter
+        mood: Optional mood filter
+        limit: Maximum results
+
+    Returns:
+        List of matching AudioTrack instances
+    """
+    search_query = db.query(AudioTrack)
+
+    # Text search on multiple fields
+    search_term = f"%{query.lower()}%"
+    search_query = search_query.filter(
+        or_(
+            func.lower(AudioTrack.filename).like(search_term),
+            func.lower(AudioTrack.genre_primary).like(search_term),
+            func.lower(AudioTrack.mood_primary).like(search_term),
+            AudioTrack.instruments.op('&&')(f'{{{query.lower()}}}'),  # Array overlap
+        )
+    )
+
+    # Apply additional filters
+    if genre:
+        search_query = search_query.filter(
+            or_(
+                AudioTrack.genre_primary == genre,
+                AudioTrack.genre_secondary.contains([genre])
+            )
+        )
+
+    if mood:
+        search_query = search_query.filter(
+            or_(
+                AudioTrack.mood_primary == mood,
+                AudioTrack.mood_secondary.contains([mood])
+            )
+        )
+
+    # Order by relevance (simple: by filename match first)
+    search_query = search_query.order_by(AudioTrack.analyzed_at.desc())
+
+    return search_query.limit(limit).all()
+
+
+def get_similar_tracks(
+    db: Session,
+    track_id: UUID,
+    limit: int = 10,
+) -> List[AudioTrack]:
+    """Get tracks similar to the given track.
+
+    Args:
+        db: Database session
+        track_id: Reference track ID
+        limit: Maximum results
+
+    Returns:
+        List of similar AudioTrack instances
+
+    Note:
+        If embeddings are available, uses vector similarity.
+        Otherwise, falls back to genre + mood + BPM similarity.
+    """
+    # Get reference track
+    ref_track = get_track_by_id(db, track_id)
+    if not ref_track:
+        return []
+
+    # TODO: Implement vector similarity when embeddings are available
+    # For now, use genre + mood + BPM similarity
+
+    query = db.query(AudioTrack).filter(AudioTrack.id != track_id)
+
+    # Same genre (primary or secondary)
+    if ref_track.genre_primary:
+        query = query.filter(
+            or_(
+                AudioTrack.genre_primary == ref_track.genre_primary,
+                AudioTrack.genre_secondary.contains([ref_track.genre_primary])
+            )
+        )
+
+    # Similar mood
+    if ref_track.mood_primary:
+        query = query.filter(
+            or_(
+                AudioTrack.mood_primary == ref_track.mood_primary,
+                AudioTrack.mood_secondary.contains([ref_track.mood_primary])
+            )
+        )
+
+    # Similar BPM (±10%)
+    if ref_track.tempo_bpm:
+        bpm_range = ref_track.tempo_bpm * 0.1
+        query = query.filter(
+            and_(
+                AudioTrack.tempo_bpm >= ref_track.tempo_bpm - bpm_range,
+                AudioTrack.tempo_bpm <= ref_track.tempo_bpm + bpm_range,
+            )
+        )
+
+    # Order by analyzed_at (could be improved with similarity score)
+    query = query.order_by(AudioTrack.analyzed_at.desc())
+
+    return query.limit(limit).all()
+
+
+def delete_track(db: Session, track_id: UUID) -> bool:
+    """Delete a track.
+
+    Args:
+        db: Database session
+        track_id: Track UUID
+
+    Returns:
+        True if deleted, False if not found
+    """
+    track = get_track_by_id(db, track_id)
+    if not track:
+        return False
+
+    db.delete(track)
+    db.commit()
+
+    logger.info(f"Deleted track: {track_id}")
+    return True
+
+
+def get_stats(db: Session) -> Dict:
+    """Get database statistics.
+
+    Args:
+        db: Database session
+
+    Returns:
+        Dictionary with statistics
+    """
+    total_tracks = db.query(func.count(AudioTrack.id)).scalar()
+
+    # Genre distribution
+    genre_counts = (
+        db.query(AudioTrack.genre_primary, func.count(AudioTrack.id))
+        .filter(AudioTrack.genre_primary.isnot(None))
+        .group_by(AudioTrack.genre_primary)
+        .order_by(func.count(AudioTrack.id).desc())
+        .limit(10)
+        .all()
+    )
+
+    # Mood distribution
+    mood_counts = (
+        db.query(AudioTrack.mood_primary, func.count(AudioTrack.id))
+        .filter(AudioTrack.mood_primary.isnot(None))
+        .group_by(AudioTrack.mood_primary)
+        .order_by(func.count(AudioTrack.id).desc())
+        .limit(10)
+        .all()
+    )
+
+    # Average BPM
+    avg_bpm = db.query(func.avg(AudioTrack.tempo_bpm)).scalar()
+
+    # Total duration
+    total_duration = db.query(func.sum(AudioTrack.duration_seconds)).scalar()
+
+    return {
+        "total_tracks": total_tracks or 0,
+        "genres": [{"genre": g, "count": c} for g, c in genre_counts],
+        "moods": [{"mood": m, "count": c} for m, c in mood_counts],
+        "average_bpm": round(float(avg_bpm), 1) if avg_bpm else 0.0,
+        "total_duration_hours": round(float(total_duration) / 3600, 1) if total_duration else 0.0,
+    }
+
+
+def upsert_track(db: Session, analysis: AudioAnalysis) -> AudioTrack:
+    """Create or update track (based on filepath).
+
+    Args:
+        db: Database session
+        analysis: AudioAnalysis object
+
+    Returns:
+        AudioTrack instance
+    """
+    # Check if track already exists
+    existing_track = get_track_by_filepath(db, analysis.filepath)
+
+    if existing_track:
+        # Update existing track
+        for key, value in analysis.dict(exclude={'filepath'}).items():
+            setattr(existing_track, key, value)
+
+        db.commit()
+        db.refresh(existing_track)
+
+        logger.info(f"Updated track: {existing_track.id} - {existing_track.filename}")
+        return existing_track
+
+    else:
+        # Create new track
+        return create_track(db, analysis)
--- a/backend/src/models/database.py
+++ b/backend/src/models/database.py
@@ -0,0 +1,47 @@
+"""Database connection and session management."""
+from sqlalchemy import create_engine
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker, Session
+from typing import Generator
+
+from ..utils.config import settings
+
+# Create SQLAlchemy engine
+engine = create_engine(
+    settings.DATABASE_URL,
+    pool_pre_ping=True,  # Enable connection health checks
+    echo=settings.DEBUG,  # Log SQL queries in debug mode
+)
+
+# Create session factory
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+# Base class for models
+Base = declarative_base()
+
+
+def get_db() -> Generator[Session, None, None]:
+    """Dependency for getting database session.
+
+    Yields:
+        Database session
+
+    Usage:
+        @app.get("/")
+        def endpoint(db: Session = Depends(get_db)):
+            ...
+    """
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+
+def init_db() -> None:
+    """Initialize database (create tables).
+
+    Note:
+        In production, use Alembic migrations instead.
+    """
+    Base.metadata.create_all(bind=engine)
--- a/backend/src/models/schema.py
+++ b/backend/src/models/schema.py
@@ -0,0 +1,127 @@
+"""SQLAlchemy database models."""
+from datetime import datetime
+from typing import Optional, List
+from uuid import uuid4
+
+from sqlalchemy import Column, String, Float, Integer, Boolean, DateTime, JSON, ARRAY, BigInteger, Index, text
+from sqlalchemy.dialects.postgresql import UUID
+from pgvector.sqlalchemy import Vector
+
+from .database import Base
+
+
+class AudioTrack(Base):
+    """Audio track model with extracted features and classifications."""
+
+    __tablename__ = "audio_tracks"
+
+    # Primary key
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid4, server_default=text("gen_random_uuid()"))
+
+    # File information
+    filepath = Column(String, unique=True, nullable=False, index=True)
+    filename = Column(String, nullable=False)
+    duration_seconds = Column(Float, nullable=True)
+    file_size_bytes = Column(BigInteger, nullable=True)
+    format = Column(String, nullable=True)  # mp3, wav, flac, etc.
+    analyzed_at = Column(DateTime, default=datetime.utcnow, nullable=False)
+
+    # Musical features (extracted via librosa)
+    tempo_bpm = Column(Float, nullable=True, index=True)
+    key = Column(String, nullable=True)  # e.g., "C major", "D# minor"
+    time_signature = Column(String, nullable=True)  # e.g., "4/4", "3/4"
+    energy = Column(Float, nullable=True)  # 0-1
+    danceability = Column(Float, nullable=True)  # 0-1
+    valence = Column(Float, nullable=True)  # 0-1 (positivity)
+    loudness_lufs = Column(Float, nullable=True)  # LUFS
+    spectral_centroid = Column(Float, nullable=True)  # Hz
+    zero_crossing_rate = Column(Float, nullable=True)  # 0-1
+
+    # Genre classification (via Essentia)
+    genre_primary = Column(String, nullable=True, index=True)
+    genre_secondary = Column(ARRAY(String), nullable=True)
+    genre_confidence = Column(Float, nullable=True)  # 0-1
+
+    # Mood classification (via Essentia)
+    mood_primary = Column(String, nullable=True, index=True)
+    mood_secondary = Column(ARRAY(String), nullable=True)
+    mood_arousal = Column(Float, nullable=True)  # 0-1
+    mood_valence = Column(Float, nullable=True)  # 0-1
+
+    # Instrument detection (via Essentia)
+    instruments = Column(ARRAY(String), nullable=True)  # List of detected instruments
+
+    # Vocal detection (future feature)
+    has_vocals = Column(Boolean, nullable=True)
+    vocal_gender = Column(String, nullable=True)  # male, female, mixed, null
+
+    # Embeddings (optional - for CLAP/semantic search)
+    embedding = Column(Vector(512), nullable=True)  # 512D vector for CLAP
+    embedding_model = Column(String, nullable=True)  # Model name used
+
+    # Additional metadata (JSON for flexibility)
+    metadata = Column(JSON, nullable=True)
+
+    # Indexes
+    __table_args__ = (
+        Index("idx_genre_primary", "genre_primary"),
+        Index("idx_mood_primary", "mood_primary"),
+        Index("idx_tempo_bpm", "tempo_bpm"),
+        Index("idx_filepath", "filepath"),
+        # Vector index for similarity search (created via migration)
+        # Index("idx_embedding", "embedding", postgresql_using="ivfflat", postgresql_ops={"embedding": "vector_cosine_ops"}),
+    )
+
+    def __repr__(self) -> str:
+        return f"<AudioTrack(id={self.id}, filename={self.filename}, genre={self.genre_primary})>"
+
+    def to_dict(self) -> dict:
+        """Convert model to dictionary.
+
+        Returns:
+            Dictionary representation of the track
+        """
+        return {
+            "id": str(self.id),
+            "filepath": self.filepath,
+            "filename": self.filename,
+            "duration_seconds": self.duration_seconds,
+            "file_size_bytes": self.file_size_bytes,
+            "format": self.format,
+            "analyzed_at": self.analyzed_at.isoformat() if self.analyzed_at else None,
+            "features": {
+                "tempo_bpm": self.tempo_bpm,
+                "key": self.key,
+                "time_signature": self.time_signature,
+                "energy": self.energy,
+                "danceability": self.danceability,
+                "valence": self.valence,
+                "loudness_lufs": self.loudness_lufs,
+                "spectral_centroid": self.spectral_centroid,
+                "zero_crossing_rate": self.zero_crossing_rate,
+            },
+            "classification": {
+                "genre": {
+                    "primary": self.genre_primary,
+                    "secondary": self.genre_secondary or [],
+                    "confidence": self.genre_confidence,
+                },
+                "mood": {
+                    "primary": self.mood_primary,
+                    "secondary": self.mood_secondary or [],
+                    "arousal": self.mood_arousal,
+                    "valence": self.mood_valence,
+                },
+                "instruments": self.instruments or [],
+                "vocals": {
+                    "present": self.has_vocals,
+                    "gender": self.vocal_gender,
+                },
+            },
+            "embedding": {
+                "model": self.embedding_model,
+                "dimension": 512 if self.embedding else None,
+                # Don't include actual vector in API responses (too large)
+            },
+            "metadata": self.metadata or {},
+        }
--- a/backend/src/utils/init.py
+++ b/backend/src/utils/init.py
--- a/backend/src/utils/config.py
+++ b/backend/src/utils/config.py
@@ -0,0 +1,41 @@
+"""Application configuration using Pydantic Settings."""
+from typing import List
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class Settings(BaseSettings):
+    """Application settings loaded from environment variables."""
+
+    # Database
+    DATABASE_URL: str = "postgresql://audio_user:audio_password@localhost:5432/audio_classifier"
+
+    # API Configuration
+    CORS_ORIGINS: str = "http://localhost:3000,http://127.0.0.1:3000"
+    API_HOST: str = "0.0.0.0"
+    API_PORT: int = 8000
+
+    # Audio Analysis Configuration
+    ANALYSIS_USE_CLAP: bool = False
+    ANALYSIS_NUM_WORKERS: int = 4
+    ESSENTIA_MODELS_PATH: str = "./models"
+    AUDIO_LIBRARY_PATH: str = "/audio"
+
+    # Application
+    APP_NAME: str = "Audio Classifier API"
+    APP_VERSION: str = "1.0.0"
+    DEBUG: bool = False
+
+    model_config = SettingsConfigDict(
+        env_file=".env",
+        env_file_encoding="utf-8",
+        case_sensitive=True
+    )
+
+    @property
+    def cors_origins_list(self) -> List[str]:
+        """Parse CORS origins string to list."""
+        return [origin.strip() for origin in self.CORS_ORIGINS.split(",")]
+
+
+# Global settings instance
+settings = Settings()
--- a/backend/src/utils/logging.py
+++ b/backend/src/utils/logging.py
@@ -0,0 +1,30 @@
+"""Logging configuration."""
+import logging
+import sys
+from typing import Any
+
+def setup_logging(level: int = logging.INFO) -> None:
+    """Configure application logging.
+
+    Args:
+        level: Logging level (default: INFO)
+    """
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        handlers=[
+            logging.StreamHandler(sys.stdout)
+        ]
+    )
+
+
+def get_logger(name: str) -> logging.Logger:
+    """Get a logger instance.
+
+    Args:
+        name: Logger name (usually __name__)
+
+    Returns:
+        Configured logger instance
+    """
+    return logging.getLogger(name)
--- a/backend/src/utils/validators.py
+++ b/backend/src/utils/validators.py
@@ -0,0 +1,112 @@
+"""Audio file validation utilities."""
+import os
+from pathlib import Path
+from typing import List, Optional
+
+SUPPORTED_AUDIO_EXTENSIONS = {".mp3", ".wav", ".flac", ".m4a", ".ogg", ".aac"}
+
+
+def is_audio_file(filepath: str) -> bool:
+    """Check if file is a supported audio format.
+
+    Args:
+        filepath: Path to file
+
+    Returns:
+        True if file has supported audio extension
+    """
+    return Path(filepath).suffix.lower() in SUPPORTED_AUDIO_EXTENSIONS
+
+
+def validate_file_path(filepath: str) -> Optional[str]:
+    """Validate and sanitize file path.
+
+    Args:
+        filepath: Path to validate
+
+    Returns:
+        Sanitized absolute path or None if invalid
+
+    Security:
+        - Prevents path traversal attacks
+        - Resolves to absolute path
+        - Checks file exists
+    """
+    try:
+        # Resolve to absolute path
+        abs_path = Path(filepath).resolve()
+
+        # Check file exists
+        if not abs_path.exists():
+            return None
+
+        # Check it's a file (not directory)
+        if not abs_path.is_file():
+            return None
+
+        # Check it's an audio file
+        if not is_audio_file(str(abs_path)):
+            return None
+
+        return str(abs_path)
+
+    except (OSError, ValueError):
+        return None
+
+
+def validate_directory_path(dirpath: str) -> Optional[str]:
+    """Validate and sanitize directory path.
+
+    Args:
+        dirpath: Directory path to validate
+
+    Returns:
+        Sanitized absolute path or None if invalid
+
+    Security:
+        - Prevents path traversal attacks
+        - Resolves to absolute path
+        - Checks directory exists
+    """
+    try:
+        # Resolve to absolute path
+        abs_path = Path(dirpath).resolve()
+
+        # Check directory exists
+        if not abs_path.exists():
+            return None
+
+        # Check it's a directory
+        if not abs_path.is_dir():
+            return None
+
+        return str(abs_path)
+
+    except (OSError, ValueError):
+        return None
+
+
+def get_audio_files(directory: str, recursive: bool = True) -> List[str]:
+    """Get all audio files in directory.
+
+    Args:
+        directory: Directory path
+        recursive: If True, search recursively
+
+    Returns:
+        List of absolute paths to audio files
+    """
+    audio_files = []
+    dir_path = Path(directory)
+
+    if not dir_path.exists() or not dir_path.is_dir():
+        return audio_files
+
+    # Choose iterator based on recursive flag
+    iterator = dir_path.rglob("*") if recursive else dir_path.glob("*")
+
+    for file_path in iterator:
+        if file_path.is_file() and is_audio_file(str(file_path)):
+            audio_files.append(str(file_path.resolve()))
+
+    return sorted(audio_files)
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -0,0 +1,58 @@
+version: '3.8'
+
+services:
+  postgres:
+    image: pgvector/pgvector:pg16
+    container_name: audio_classifier_db
+    environment:
+      POSTGRES_USER: ${POSTGRES_USER:-audio_user}
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-audio_password}
+      POSTGRES_DB: ${POSTGRES_DB:-audio_classifier}
+    ports:
+      - "5432:5432"
+    volumes:
+      - postgres_data:/var/lib/postgresql/data
+      - ./backend/init-db.sql:/docker-entrypoint-initdb.d/init-db.sql
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-audio_user}"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+    restart: unless-stopped
+
+  backend:
+    build: ./backend
+    container_name: audio_classifier_api
+    depends_on:
+      postgres:
+        condition: service_healthy
+    environment:
+      DATABASE_URL: postgresql://${POSTGRES_USER:-audio_user}:${POSTGRES_PASSWORD:-audio_password}@postgres:5432/${POSTGRES_DB:-audio_classifier}
+      CORS_ORIGINS: ${CORS_ORIGINS:-http://localhost:3000}
+      ANALYSIS_USE_CLAP: ${ANALYSIS_USE_CLAP:-false}
+      ANALYSIS_NUM_WORKERS: ${ANALYSIS_NUM_WORKERS:-4}
+      ESSENTIA_MODELS_PATH: /app/models
+    ports:
+      - "8000:8000"
+    volumes:
+      # Mount your audio library (read-only)
+      - ${AUDIO_LIBRARY_PATH:-./audio_samples}:/audio:ro
+      # Mount models directory
+      - ./backend/models:/app/models
+    restart: unless-stopped
+
+  # Frontend (development mode - for production use static build)
+  # frontend:
+  #   build: ./frontend
+  #   container_name: audio_classifier_ui
+  #   environment:
+  #     NEXT_PUBLIC_API_URL: http://localhost:8000
+  #   ports:
+  #     - "3000:3000"
+  #   depends_on:
+  #     - backend
+  #   restart: unless-stopped
+
+volumes:
+  postgres_data:
+    driver: local
--- a/frontend/.env.local.example
+++ b/frontend/.env.local.example
@@ -0,0 +1 @@
+NEXT_PUBLIC_API_URL=http://localhost:8000
--- a/frontend/app/globals.css
+++ b/frontend/app/globals.css
@@ -0,0 +1,37 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+@layer base {
+  :root {
+    --background: 0 0% 100%;
+    --foreground: 222.2 84% 4.9%;
+    --card: 0 0% 100%;
+    --card-foreground: 222.2 84% 4.9%;
+    --popover: 0 0% 100%;
+    --popover-foreground: 222.2 84% 4.9%;
+    --primary: 221.2 83.2% 53.3%;
+    --primary-foreground: 210 40% 98%;
+    --secondary: 210 40% 96.1%;
+    --secondary-foreground: 222.2 47.4% 11.2%;
+    --muted: 210 40% 96.1%;
+    --muted-foreground: 215.4 16.3% 46.9%;
+    --accent: 210 40% 96.1%;
+    --accent-foreground: 222.2 47.4% 11.2%;
+    --destructive: 0 84.2% 60.2%;
+    --destructive-foreground: 210 40% 98%;
+    --border: 214.3 31.8% 91.4%;
+    --input: 214.3 31.8% 91.4%;
+    --ring: 221.2 83.2% 53.3%;
+    --radius: 0.5rem;
+  }
+}
+
+@layer base {
+  * {
+    @apply border-border;
+  }
+  body {
+    @apply bg-background text-foreground;
+  }
+}
--- a/frontend/app/layout.tsx
+++ b/frontend/app/layout.tsx
@@ -0,0 +1,27 @@
+import type { Metadata } from "next"
+import { Inter } from "next/font/google"
+import "./globals.css"
+import { QueryProvider } from "@/components/providers/QueryProvider"
+
+const inter = Inter({ subsets: ["latin"] })
+
+export const metadata: Metadata = {
+  title: "Audio Classifier",
+  description: "Intelligent audio library management and classification",
+}
+
+export default function RootLayout({
+  children,
+}: {
+  children: React.ReactNode
+}) {
+  return (
+    <html lang="en">
+      <body className={inter.className}>
+        <QueryProvider>
+          {children}
+        </QueryProvider>
+      </body>
+    </html>
+  )
+}
--- a/frontend/app/page.tsx
+++ b/frontend/app/page.tsx
@@ -0,0 +1,159 @@
+"use client"
+
+import { useState } from "react"
+import { useQuery } from "@tanstack/react-query"
+import { getTracks, getStats } from "@/lib/api"
+import type { FilterParams } from "@/lib/types"
+
+export default function Home() {
+  const [filters, setFilters] = useState<FilterParams>({})
+  const [page, setPage] = useState(0)
+  const limit = 50
+
+  const { data: tracksData, isLoading: isLoadingTracks } = useQuery({
+    queryKey: ['tracks', filters, page],
+    queryFn: () => getTracks({ ...filters, skip: page * limit, limit }),
+  })
+
+  const { data: stats } = useQuery({
+    queryKey: ['stats'],
+    queryFn: getStats,
+  })
+
+  return (
+    <div className="min-h-screen bg-gray-50">
+      {/* Header */}
+      <header className="bg-white border-b">
+        <div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-4">
+          <h1 className="text-3xl font-bold text-gray-900">Audio Classifier</h1>
+          <p className="text-gray-600">Intelligent music library management</p>
+        </div>
+      </header>
+
+      {/* Main Content */}
+      <main className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-8">
+        {/* Stats */}
+        {stats && (
+          <div className="grid grid-cols-1 md:grid-cols-4 gap-4 mb-8">
+            <div className="bg-white p-4 rounded-lg shadow">
+              <p className="text-gray-600 text-sm">Total Tracks</p>
+              <p className="text-2xl font-bold">{stats.total_tracks}</p>
+            </div>
+            <div className="bg-white p-4 rounded-lg shadow">
+              <p className="text-gray-600 text-sm">Avg BPM</p>
+              <p className="text-2xl font-bold">{stats.average_bpm}</p>
+            </div>
+            <div className="bg-white p-4 rounded-lg shadow">
+              <p className="text-gray-600 text-sm">Total Hours</p>
+              <p className="text-2xl font-bold">{stats.total_duration_hours}h</p>
+            </div>
+            <div className="bg-white p-4 rounded-lg shadow">
+              <p className="text-gray-600 text-sm">Genres</p>
+              <p className="text-2xl font-bold">{stats.genres.length}</p>
+            </div>
+          </div>
+        )}
+
+        {/* Tracks List */}
+        <div className="bg-white rounded-lg shadow">
+          <div className="p-4 border-b">
+            <h2 className="text-xl font-semibold">Music Library</h2>
+            <p className="text-gray-600 text-sm">
+              {tracksData?.total || 0} tracks total
+            </p>
+          </div>
+
+          {isLoadingTracks ? (
+            <div className="p-8 text-center text-gray-600">Loading...</div>
+          ) : tracksData?.tracks.length === 0 ? (
+            <div className="p-8 text-center text-gray-600">
+              No tracks found. Start by analyzing your audio library!
+            </div>
+          ) : (
+            <div className="divide-y">
+              {tracksData?.tracks.map((track) => (
+                <div key={track.id} className="p-4 hover:bg-gray-50">
+                  <div className="flex justify-between items-start">
+                    <div className="flex-1">
+                      <h3 className="font-medium text-gray-900">{track.filename}</h3>
+                      <div className="mt-1 flex flex-wrap gap-2">
+                        <span className="inline-flex items-center px-2 py-1 rounded text-xs bg-blue-100 text-blue-800">
+                          {track.classification.genre.primary}
+                        </span>
+                        <span className="inline-flex items-center px-2 py-1 rounded text-xs bg-purple-100 text-purple-800">
+                          {track.classification.mood.primary}
+                        </span>
+                        <span className="text-xs text-gray-500">
+                          {Math.round(track.features.tempo_bpm)} BPM
+                        </span>
+                        <span className="text-xs text-gray-500">
+                          {Math.floor(track.duration_seconds / 60)}:{String(Math.floor(track.duration_seconds % 60)).padStart(2, '0')}
+                        </span>
+                      </div>
+                    </div>
+                    <div className="ml-4 flex gap-2">
+                      <a
+                        href={`${process.env.NEXT_PUBLIC_API_URL}/api/audio/stream/${track.id}`}
+                        target="_blank"
+                        rel="noopener noreferrer"
+                        className="px-3 py-1 text-sm bg-blue-600 text-white rounded hover:bg-blue-700"
+                      >
+                        Play
+                      </a>
+                      <a
+                        href={`${process.env.NEXT_PUBLIC_API_URL}/api/audio/download/${track.id}`}
+                        download
+                        className="px-3 py-1 text-sm bg-gray-600 text-white rounded hover:bg-gray-700"
+                      >
+                        Download
+                      </a>
+                    </div>
+                  </div>
+                </div>
+              ))}
+            </div>
+          )}
+
+          {/* Pagination */}
+          {tracksData && tracksData.total > limit && (
+            <div className="p-4 border-t flex justify-between items-center">
+              <button
+                onClick={() => setPage(p => Math.max(0, p - 1))}
+                disabled={page === 0}
+                className="px-4 py-2 bg-gray-200 rounded disabled:opacity-50"
+              >
+                Previous
+              </button>
+              <span className="text-sm text-gray-600">
+                Page {page + 1} of {Math.ceil(tracksData.total / limit)}
+              </span>
+              <button
+                onClick={() => setPage(p => p + 1)}
+                disabled={(page + 1) * limit >= tracksData.total}
+                className="px-4 py-2 bg-gray-200 rounded disabled:opacity-50"
+              >
+                Next
+              </button>
+            </div>
+          )}
+        </div>
+
+        {/* Instructions */}
+        <div className="mt-8 bg-blue-50 border border-blue-200 rounded-lg p-6">
+          <h3 className="font-semibold text-blue-900 mb-2">Getting Started</h3>
+          <ol className="list-decimal list-inside space-y-1 text-blue-800 text-sm">
+            <li>Make sure the backend is running (<code>docker-compose up</code>)</li>
+            <li>Use the API to analyze your audio library:
+              <pre className="mt-2 bg-blue-100 p-2 rounded text-xs">
+                {`curl -X POST http://localhost:8000/api/analyze/folder \\
+  -H "Content-Type: application/json" \\
+  -d '{"path": "/audio/your_music", "recursive": true}'`}
+              </pre>
+            </li>
+            <li>Refresh this page to see your analyzed tracks</li>
+          </ol>
+        </div>
+      </main>
+    </div>
+  )
+}
--- a/frontend/components/providers/QueryProvider.tsx
+++ b/frontend/components/providers/QueryProvider.tsx
@@ -0,0 +1,24 @@
+"use client"
+
+import { QueryClient, QueryClientProvider } from "@tanstack/react-query"
+import { ReactNode, useState } from "react"
+
+export function QueryProvider({ children }: { children: ReactNode }) {
+  const [queryClient] = useState(
+    () =>
+      new QueryClient({
+        defaultOptions: {
+          queries: {
+            staleTime: 60 * 1000, // 1 minute
+            refetchOnWindowFocus: false,
+          },
+        },
+      })
+  )
+
+  return (
+    <QueryClientProvider client={queryClient}>
+      {children}
+    </QueryClientProvider>
+  )
+}
--- a/frontend/next.config.js
+++ b/frontend/next.config.js
@@ -0,0 +1,6 @@
+/** @type {import('next').NextConfig} */
+const nextConfig = {
+  reactStrictMode: true,
+}
+
+module.exports = nextConfig
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -0,0 +1,35 @@
+{
+  "name": "audio-classifier-frontend",
+  "version": "1.0.0",
+  "private": true,
+  "scripts": {
+    "dev": "next dev",
+    "build": "next build",
+    "start": "next start",
+    "lint": "next lint"
+  },
+  "dependencies": {
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1",
+    "next": "^15.1.0",
+    "@tanstack/react-query": "^5.28.0",
+    "axios": "^1.6.7",
+    "zustand": "^4.5.1",
+    "lucide-react": "^0.344.0",
+    "recharts": "^2.12.0",
+    "class-variance-authority": "^0.7.0",
+    "clsx": "^2.1.0",
+    "tailwind-merge": "^2.2.1"
+  },
+  "devDependencies": {
+    "typescript": "^5.3.3",
+    "@types/node": "^20.11.19",
+    "@types/react": "^18.2.55",
+    "@types/react-dom": "^18.2.19",
+    "autoprefixer": "^10.4.17",
+    "postcss": "^8.4.35",
+    "tailwindcss": "^3.4.1",
+    "eslint": "^8.56.0",
+    "eslint-config-next": "^15.1.0"
+  }
+}
--- a/frontend/postcss.config.js
+++ b/frontend/postcss.config.js
@@ -0,0 +1,6 @@
+module.exports = {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+}
--- a/frontend/tailwind.config.ts
+++ b/frontend/tailwind.config.ts
@@ -0,0 +1,55 @@
+import type { Config } from "tailwindcss"
+
+const config: Config = {
+  content: [
+    "./pages/**/*.{js,ts,jsx,tsx,mdx}",
+    "./components/**/*.{js,ts,jsx,tsx,mdx}",
+    "./app/**/*.{js,ts,jsx,tsx,mdx}",
+  ],
+  theme: {
+    extend: {
+      colors: {
+        border: "hsl(var(--border))",
+        input: "hsl(var(--input))",
+        ring: "hsl(var(--ring))",
+        background: "hsl(var(--background))",
+        foreground: "hsl(var(--foreground))",
+        primary: {
+          DEFAULT: "hsl(var(--primary))",
+          foreground: "hsl(var(--primary-foreground))",
+        },
+        secondary: {
+          DEFAULT: "hsl(var(--secondary))",
+          foreground: "hsl(var(--secondary-foreground))",
+        },
+        destructive: {
+          DEFAULT: "hsl(var(--destructive))",
+          foreground: "hsl(var(--destructive-foreground))",
+        },
+        muted: {
+          DEFAULT: "hsl(var(--muted))",
+          foreground: "hsl(var(--muted-foreground))",
+        },
+        accent: {
+          DEFAULT: "hsl(var(--accent))",
+          foreground: "hsl(var(--accent-foreground))",
+        },
+        popover: {
+          DEFAULT: "hsl(var(--popover))",
+          foreground: "hsl(var(--popover-foreground))",
+        },
+        card: {
+          DEFAULT: "hsl(var(--card))",
+          foreground: "hsl(var(--card-foreground))",
+        },
+      },
+      borderRadius: {
+        lg: "var(--radius)",
+        md: "calc(var(--radius) - 2px)",
+        sm: "calc(var(--radius) - 4px)",
+      },
+    },
+  },
+  plugins: [],
+}
+export default config
--- a/frontend/tsconfig.json
+++ b/frontend/tsconfig.json
@@ -0,0 +1,26 @@
+{
+  "compilerOptions": {
+    "lib": ["dom", "dom.iterable", "esnext"],
+    "allowJs": true,
+    "skipLibCheck": true,
+    "strict": true,
+    "noEmit": true,
+    "esModuleInterop": true,
+    "module": "esnext",
+    "moduleResolution": "bundler",
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "jsx": "preserve",
+    "incremental": true,
+    "plugins": [
+      {
+        "name": "next"
+      }
+    ],
+    "paths": {
+      "@/*": ["./*"]
+    }
+  },
+  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
+  "exclude": ["node_modules"]
+}
--- a/scripts/download-essentia-models.sh
+++ b/scripts/download-essentia-models.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+
+# Download Essentia models for audio classification
+# Models from: https://essentia.upf.edu/models.html
+
+set -e  # Exit on error
+
+MODELS_DIR="backend/models"
+BASE_URL="https://essentia.upf.edu/models/classification-heads"
+
+echo "📦 Downloading Essentia models..."
+echo "Models directory: $MODELS_DIR"
+
+# Create models directory if it doesn't exist
+mkdir -p "$MODELS_DIR"
+
+# Model files
+declare -A MODELS
+MODELS=(
+    ["mtg_jamendo_genre-discogs-effnet-1.pb"]="$BASE_URL/mtg_jamendo_genre/mtg_jamendo_genre-discogs-effnet-1.pb"
+    ["mtg_jamendo_moodtheme-discogs-effnet-1.pb"]="$BASE_URL/mtg_jamendo_moodtheme/mtg_jamendo_moodtheme-discogs-effnet-1.pb"
+    ["mtg_jamendo_instrument-discogs-effnet-1.pb"]="$BASE_URL/mtg_jamendo_instrument/mtg_jamendo_instrument-discogs-effnet-1.pb"
+)
+
+# Download each model
+for model_file in "${!MODELS[@]}"; do
+    url="${MODELS[$model_file]}"
+    output_path="$MODELS_DIR/$model_file"
+
+    if [ -f "$output_path" ]; then
+        echo "✓ $model_file already exists, skipping..."
+    else
+        echo "⬇️  Downloading $model_file..."
+        curl -L -o "$output_path" "$url"
+
+        if [ -f "$output_path" ]; then
+            echo "✓ Downloaded $model_file"
+        else
+            echo "✗ Failed to download $model_file"
+            exit 1
+        fi
+    fi
+done
+
+echo ""
+echo "✅ All models downloaded successfully!"
+echo ""
+echo "Models available:"
+ls -lh "$MODELS_DIR"/*.pb 2>/dev/null || echo "No .pb files found"
+
+echo ""
+echo "Note: Class labels are defined in backend/src/core/essentia_classifier.py"
+echo "You can now start the backend with: docker-compose up"