Parametric Time-Frequency Analysis and Its Applications in Music Classification
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Research Article Parametric Time-Frequency Analysis and Its Applications in Music Classification Ying Shen, Xiaoli Li, Ngok-Wah Ma, and Sridhar Krishnan Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada M5B 2K3 Correspondence should be addressed to Sridhar Krishnan, [email protected] Received 14 February 2010; Revised 15 July 2010; Accepted 15 August 2010 Academic Editor: Yimin Zhang Copyright © 2010 Ying Shen et al. 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. 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.
1. Introduction Since most of the real-world signals are non-stationary, the study and analysis of non-stationary signals is receiving more and more attention in the scientific community. For signal analysis, time series and frequency spectrum contain all the information about the underlying processes of signals. But by themselves, the best representations of non-stationary processes may not be well presented. Due to the time-varying behavior, techniques which give joint time frequency (TF) information are needed to analyze non-stationary signals. Gabor introduced the concept of atoms and stated that any signal could be described as a superimposition of a large number of such atoms [1]. Atoms, also called basis functions, are signals localized in both time and frequency domains. This signal analysis method devises a joint function of time and frequency, that is, a distribution that will describe the energy density or intensity of a signal simultaneously in time and frequency [2]. Features
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