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Onset Detection in Audio Music J.-S Roger Jang ( 張智星 ) http://mirlab.org/jang MIR LabMIR Lab, CSIE Dept. National Taiwan University
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-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.
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-3- Difficulty in Onset Detection Music types Monophonic Easier Polyphonic Harder Instrument types Percussive instruments Easier String instruments Harder (soft onsets)
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-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)
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-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
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-6- ODF: Spectral Flux Concept sum the positive change in each frequency bin
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-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.
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-8- Example of OSC Try “wave2osc.m”
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-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
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-10- Beat Tracking Demos http://mirlab.org/demo/beatTracking http://mirlab.org/demo/beatTracking Try “beatTracking.m” in SAP toolbox
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-11- Example of Beat Tracking beatTracking.m
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-12- Performance Indices of Beat 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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