Abstract
This paper describes a new method for music onset detection. The novelty of the approach consists mainly of two elements: the time-frequency processing and the detection stages. The resonator time frequency image (RTFI) is the basic time-frequency analysis tool. The time-frequency processing part is in charge of transforming the RTFI energy spectrum into more natural energy- change and pitch-change cues that are then used as input elements for the detection of music onsets by detection tools. Two detection algorithms have been developed: an energy-based algorithm and a pitch-based one. The energy-based detection algorithm exploits energy-change cues and performs particularly well for the detection of hard onsets. The pitch-based algorithm successfully exploits stable pitch cues for the onset detection in polyphonic music, and achieves much better performances than the energy-based algorithm when applied to the detection of soft onsets. Results for both the energy-based and pitch-based detection algorithms have been obtained on a large music dataset.
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