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  • Research Article
  • Open Access

Parametric Time-Frequency Analysis and Its Applications in Music Classification

EURASIP Journal on Advances in Signal Processing20102010:380349

  • Received: 14 February 2010
  • Accepted: 15 August 2010
  • Published:


Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.


  • Linear Discriminant Analysis
  • High Frequency Range
  • Discriminant Feature
  • Match Pursuit
  • Discriminant Information

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Authors’ Affiliations

Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada, M5B 2K3


© Ying Shen et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.