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A Discriminative Model for Polyphonic Piano Transcription

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 048317 (2006) Cite this article

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.

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Affiliations

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

    Graham E. Poliner & Daniel P. W. Ellis

Authors
  1. Graham E. Poliner
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  2. Daniel P. W. Ellis
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Corresponding author

Correspondence to Graham E. Poliner.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Poliner, G.E., Ellis, D.P.W. A Discriminative Model for Polyphonic Piano Transcription. EURASIP J. Adv. Signal Process. 2007, 048317 (2006). https://doi.org/10.1155/2007/48317

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Keywords

  • Support Vector Machine
  • Information Technology
  • Support Vector
  • Markov Model
  • Spectral Feature