# Music Information Retrieval

> Use when implementing music information retrieval.

- Skill: `loopyluci/music-information-retrieval` (Agent Skill)
- Install (CLI): `npx skillmds@latest add loopyluci/music-information-retrieval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/music-information-retrieval/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: LoopyLuci (https://skillmd.com/u/loopyluci)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/loopyluci/music-information-retrieval

---


# Music Information Retrieval

Implementing music information retrieval (MIR) — from feature extraction (chroma, tempo, beat tracking) through genre classification, chord detection, and music recommendation.

## When to Use

- Analyzing musical audio (tempo, key, chords, beats)
- Building music recommendation systems
- Automatic music transcription
- Music similarity search
- Playlist generation

## MIR Features

```python
class MIRFeatureExtractor:
    """Extract musical features from audio."""
    
    @staticmethod
    def chroma_features(waveform, sr: int = 22050) -> np.array:
        """Chroma (pitch class) features — 12-bin per octave."""
        import librosa
        return librosa.feature.chroma_stft(y=waveform, sr=sr)
    
    @staticmethod
    def beat_tracking(waveform, sr: int = 22050) -> Dict:
        import librosa
        tempo, beats = librosa.beat.beat_track(y=waveform, sr=sr)
        return {'tempo_bpm': round(tempo, 1), 'n_beats': len(beats)}
    
    @staticmethod
    def spectral_features(waveform, sr: int = 22050) -> Dict:
        import librosa
        return {
            'centroid': float(librosa.feature.spectral_centroid(y=waveform, sr=sr).mean()),
            'bandwidth': float(librosa.feature.spectral_bandwidth(y=waveform, sr=sr).mean()),
            'rolloff': float(librosa.feature.spectral_rolloff(y=waveform, sr=sr).mean()),
            'zcr': float(librosa.feature.zero_crossing_rate(waveform).mean()),
        }
```

## Verification Checklist

- [ ] Audio format standardized (sample rate, channels, duration)
- [ ] Feature extraction (chroma, tempo, spectral, MFCC) working
- [ ] Beat tracking accuracy tested on varied genres
- [ ] Key/chord detection validated against labeled dataset
- [ ] Genre classification accuracy benchmarked
- [ ] Music similarity metric defined and tested
- [ ] Real-time processing for interactive applications