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Improving generalization for polyphonic piano transcription

Poliner, Graham E.; Ellis, Daniel P. W.

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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Title
2007 Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), October 21-24, 2007, New Paltz, NY
Publisher
IEEE
DOI
https://doi.org/10.1109/ASPAA.2007.4393050

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Academic Units
Electrical Engineering
Published Here
June 27, 2012