LoopyLuci 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
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