Improving generalization for polyphonic piano transcription
Graham E. Poliner; Daniel P. W. Ellis
- Improving generalization for polyphonic piano transcription
Poliner, Graham E.
Ellis, Daniel P. W.
- Electrical Engineering
- Permanent URL:
- Book/Journal Title:
- 2007 Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), October 21-24, 2007, New Paltz, NY
- Publisher Location:
- Piscataway, N.J.
- In this paper, we present methods to improve the generalization capabilities of a classification-based approach to polyphonic piano transcription. Support vector machines trained on spectral features are used to classify frame-level note instances, and the independent classifications are temporally constrained via hidden Markov model post-processing. Semi-supervised learning and multiconditioning are investigated, and transcription results are reported for a compiled set of piano recordings. A reduction in the frame-level transcription error score of 10% was achieved by combining multiconditioning and semi-supervised classification.
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