AdaMast: A Drum Sound Recognizer
based on Adaptation and Matching of Spectrogram Templates

We propose a template-matching-based system, called AdaMast, that detects onset times of the bass drum, snare drum, and hi-hat cymbals in polyphonic audio signals of popular songs. AdaMast uses the power spectrograms of the drum sounds as templates. However, there are two main problems in transcribing drum sounds in the presence of other sounds. The first problem is that actual drum-sound spectrograms cannot be prepared as templates beforehand for each song. The second problem is that power spectrograms of sound mixtures including the drum sound are greatly different from the template (pure drum-sound spectrogram). To solve the first problem, a template-adaptation algorithm is built into AdaMast. To solve the second problem, a distance measure used in the template matching is designed to be robust to the spectral overlapping of other sounds. The test results in Audio Drum Detection Contest were 72.8%, 70.2%, and 57.4% in transcribing the bass drums, snare drums, and hi-hat cymbals, respectively, and AdaMast won the contest.

• Poster
• Summary

• Kazuyoshi Yoshii, Masataka Goto, Hiroshi G. Okuno: "Drum Sound Recognition for Polyphonic Audio Signals by Adaptation and Matching of Spectrogram Templates with Harmonic Structure Suppression," IEEE Transactions on Audio, Speech and Language Processing, Vol.15, No.1, Jan. 2007.

This research was achieved by using RWC Music Database. We thank everyone who has made this database.