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Onset Detection in Audio Music

Onset Detection in Audio Music. J.-S Roger Jang ( 張智星 ) http://mirlab.org/jang MIR Lab , CSIE Dept. National Taiwan University. What Are Note Onsets?. Energy profile of a percussive instrument is modeled as ADSR stages

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Onset Detection in Audio Music

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  1. Onset Detection in Audio Music J.-S Roger Jang (張智星) http://mirlab.org/jang MIR Lab, CSIE Dept. National Taiwan University

  2. What Are Note Onsets? • Energy profile of a percussive instrument is modeled as ADSR stages • Note onset is the time where the slope is the highest, during the attack time. • Soft onsets via violin, etc, are much harder to define and detect.

  3. Difficulty in Onset Detection • Music types • Monophonic  Easier • Polyphonic  Harder • Instrument types • Percussive instruments  Easier • String instruments  Harder (soft onsets)

  4. Why Onset Detection is Useful? • It is a basic step in music analysis • Music transcription (from wave to midi) • Music editing (Song segmentation) • Tempo estimation • Beat tracking • Musical fingerprinting (the onset trace can serve as a robust id for fingerprinting)

  5. Onset Detection Function • ODF (onset detection function) creates a curve of onset strength, aka • Onset strength curve • Novelty curve • Most ODFs are based on time-frequency representation (spectrogram) of • Magnitude of STFT (Short-time Fourier transform) • Phase of STFT • Mel-band of STFT • Constant-Q transform

  6. ODF: Spectral Flux • Concept • sum the positive change in each frequency bin

  7. Flowchart of OSC • Steps of OSC • Spectrogram • Mel-band spectrogram • Spectral flux • Smoothed OSC via Gaussian smoothing • Trend of OSC via Gaussian smoothing • Trend-subtracted OSC • Check out wave2osc.m to see these steps.

  8. Example of OSC • Try “wave2osc.m”

  9. What Can You Do With OSC... • OSC  onsets • Pick peaks to have onsets • OSC  tempo (BPM, beats per minute) • Apply ACF (or other PDF) to find the BPM • OSC  beat tracking • Pick equal-spaced peaks to have beat positions

  10. Beat Tracking • Demos • http://mirlab.org/demo/beatTracking • Try “beatTracking.m” in SAP toolbox

  11. Example of Beat Tracking • beatTracking.m

  12. Performance Indices ofBeat Tracking • Many performance indices of BT • Check out audio beat tracking task of MIREX • Mostly adopted ones • Precision, recall, f-measure, accuracy • Try simSequence.m in SAP toolbox Precision = tp/(tp+fp)=3/(3+3) = 0.5 Recall = tp/(tp+fn)=3/(3+2) = 0.6 F-measure = tp/(tp+(fn+fp)/2)=3/(3+(2+3)/2) = 0.545 Accuracy = tp/(tp+fn+fp)=3/(3+2+3) = 0.375

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