NDLI: Music genre classification using modulation spectral features and multiple prototype vectors representation

Content Provider	IEEE Xplore Digital Library
Author	Chang-Hsing Lee Chih-Hsun Chou Cheng-Chang Lien Jen-Cheng Fang
Copyright Year	2011
Description	Author affiliation: Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu, Taiwan (Chang-Hsing Lee; Chih-Hsun Chou; Cheng-Chang Lien; Jen-Cheng Fang)
Abstract	In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. A modulation spectrogram corresponding to the collection of modulation spectra of MFCC/OSC/NASE will be constructed. The modulation spectrum is then decomposed into several logarithmically spaced modulation subbands. For each modulation subband, a new set of modulation spectral features, including modulation spectral contrast (MSC), modulation spectral valley (MSV), modulation spectral energy (MSE), modulation spectral centroid (MSCEN) and modulation spectral flatness (MSF) are then computed from each modulation subband. To cope with the problem that the feature vectors extracted from the music tracks of identical music genre might differ significantly, each music genre is modeled with a number of representative prototype vectors generated by c-means clustering algorithm. An information fusion approach which integrates both feature level fusion method and decision level combination method is then employed to improve the classification accuracy. Experiments conducted on ISMIR 2004 music dataset have shown that our proposed approach can achieve higher classification accuracy than other approaches with the same experimental setup.
Starting Page	2762
Ending Page	2766
File Size	294714
Page Count	5
File Format	PDF
ISBN	9781424493043
e-ISBN	9781424493067
DOI	10.1109/CISP.2011.6100759
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mel-frequency cepstral coefficients modulation spectral analysis music genre classification normalized audio spectrum envelope Modulation Music Support vector machine classification octave-based spectral contrast Feature extraction Vectors Multiple signal classification Mel frequency cepstral coefficient
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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