Note onset detection for the transcription of polyphonic piano music

Authors:
C. G. v. d. Boogaart;R. Lienhart
Affiliations:
Multimedia Computing Lab, University of Augsburg, Augsburg, Germany;Multimedia Computing Lab, University of Augsburg, Augsburg, Germany
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

Transcription of music is the process of generating a symbolic representation such as a score sheet or a MIDI file from an audio recording of a piece of music. A statistical machine learning approach for detecting note onsets in polyphonic piano music is presented. An area from the spectrogram of the sound is concatenated into one feature vector. A cascade of boosted classifiers is used for dimensionality reduction and classification in an one-versus-all manner. The presented system achieves an accuracy of 87.4% in onset detection outperforming the best comparison system by 25.1 %.