Improving generalization for polyphonic piano transcription

Articles:

Title:: Improving generalization for polyphonic piano transcription
Author(s):: Poliner, Graham E.
Ellis, Daniel P. W.
Date:: 2007
Type:: Articles
Department:: Electrical Engineering
Permanent URL:: http://hdl.handle.net/10022/AC:P:13674
Book/Journal Title:: 2007 Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), October 21-24, 2007, New Paltz, NY
Publisher:: IEEE
Publisher Location:: Piscataway, N.J.
Abstract:: 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.
Subject(s):: Electrical engineering
Artificial intelligence
DOI:: 10.1109/ASPAA.2007.4393050