Transcription of polyphonic piano music by means of memory-based classification method

Authors:
Giovanni Costantini;Massimiliano Todisco;Renzo Perfetti
Affiliations:
Department of Electronic Engineering, University of Rome “Tor Vergata” and Institute of Acoustics “O. M. Corbino”, Rome;Department of Electronic Engineering, University of Rome “Tor Vergata”;Department of Electronic and Information Engineering, University of Perugia
Venue:
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
Year:
2009

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Abstract

Music transcription consists in transforming the musical content of audio data into a symbolic representation. The objective of this study is to investigate a transcription system for polyphonic piano. The proposed method focuses on temporal musical structures, note events and their main characteristics: the attack instant and the pitch. Onset detection exploits a time-frequency representation of the audio signal. Note classification is based on constant Q transform (CQT) and support vector machines (SVMs). Finally, to validate our method, we present a collection of experiments using a wide number of musical pieces of heterogeneous styles.