A discriminative model for polyphonic piano transcription

  • Authors:
  • Graham E. Poliner;Daniel P. W. Ellis

  • Affiliations:
  • Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, New York, NY;Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, New York, NY

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2007

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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.