Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Time-Frequency Analysis and Its Applications in Music ClassificationResearch Article Parametric Time-Frequency Analysis and Its Applications in Music Classification | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 380349 9 pages doi 2010 380349 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 krishnan@ 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 . 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 .
