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Improving generalization for polyphonic piano transcription

Graham E. Poliner; Daniel P. W. Ellis

Title:
Improving generalization for polyphonic piano transcription
Author(s):
Poliner, Graham E.
Ellis, Daniel P. W.
Date:
Type:
Articles
Department:
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:
IEEE
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
Publisher DOI:
10.1109/ASPAA.2007.4393050
Item views:
49
Metadata:
text | xml

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