EURASIP Journal on Advances in Signal Processing
Volume 2007 (2007), Article ID 48317, 9 pages
doi:10.1155/2007/48317
Research Article

A Discriminative Model for Polyphonic Piano Transcription

Graham E. Poliner and Daniel P. W. Ellis

Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, New York 10027, NY, USA

Received 6 December 2005; Revised 17 June 2006; Accepted 29 June 2006

Academic Editor: Masataka Goto

Copyright © 2007 Graham E. Poliner and Daniel P. W. Ellis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We present a discriminative model for polyphonic piano transcription. Support vector machines trained on spectral features are used to classify frame-level note instances. The classifier outputs are temporally constrained via hidden Markov models, and the proposed system is used to transcribe both synthesized and real piano recordings. A frame-level transcription accuracy of 68% was achieved on a newly generated test set, and direct comparisons to previous approaches are provided.