Evaluation of Onset Detection Algorithms in Popular Polyphonic Music on a Large Scale Database
This paper introduces a large database of popular polyphonic music containing drums (10,238 onsets) for the evaluation of onset detection algorithms. The database has been manually annotated by expert listeners. The inter-rater variability leads to an understanding of interhuman variations. Four common detection functions are investigated: spectral difference, high frequency content, phase deviation and the psychoacoustic one of Klapuri. We present an additional detection function based on the mpeg7 feature audio spectrum envelope. An adaptive peak picker determines the onsets which are compared with the manual labels. Results show that detection functions based on spectral difference obtain observable better results. The study provides a thorough investigation of onset detection algorithms in popular polyphonic music.
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