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Structured Prediction Models for Chord Transcription of Music Audio

Adrian Vivian Weller; Daniel P. W. Ellis; Tony Jebara

Title:
Structured Prediction Models for Chord Transcription of Music Audio
Author(s):
Weller, Adrian Vivian
Ellis, Daniel P. W.
Jebara, Tony
Date:
Type:
Articles
Department:
Electrical Engineering
Permanent URL:
Book/Journal Title:
The Eighth International Conference on Machine Learning and Applications: Proceedings: Miami Beach, Florida : 13-15 December 2009
Book Author:
Wani, M. Arif
Publisher:
IEEE
Publisher Location:
Piscataway, N.J.
Abstract:
Chord sequences are a compact and useful description of music, representing each beat or measure in terms of a likely distribution over individual notes without specifying the notes exactly. Transcribing music audio into chord sequences is essential for harmonic analysis, and would be an important component in content-based retrieval and indexing, but accuracy rates remain fairly low. In this paper, the existing 2008 LabROSA Supervised Chord Recognition System is modified by using different machine learning methods for decoding structural information, thereby achieving significantly superior results. Specifically, the hidden Markov model is replaced by a large margin structured prediction approach (SVMstruct) using an enlarged feature space. Performance is significantly improved by incorporating features from future (but not past) frames. The benefit of SVMstruct increases with the size of the training set, as might be expected when comparing discriminative and generative models. Without yet exploring non-linear kernels, these improvements lead to state-of-the-art performance in chord transcription. The techniques could prove useful in other sequential learning tasks which currently employ HMMs.
Subject(s):
Electrical engineering
Applied mathematics
Publisher DOI:
http://dx.doi.org/10.1109/ICMLA.2009.132
Item views:
80
Metadata:
text | xml

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