Music-genre classification system based on spectro-temporal features and feature selection

An automatic classification system of the music genres is proposed. Based on the timbre features such as mel-frequency cepstral coefficients, the spectro-temporal features are obtained to capture the temporal evolution and variation of the spectral characteristics of the music signal. Mean, variance...

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Bibliographic Details
Published in:IEEE transactions on consumer electronics 2012-11, Vol.58 (4), p.1262-1268
Main Authors: Lim, Shin-Cheol, Lee, Jong-Seol, Jang, Sei-Jin, Lee, Soek-Pil, Kim, Moo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Electronics
Feature extraction
feature selection
Mathematical analysis
Mel frequency cepstral coefficient
Modulation
modulation spectrum
Music
Music genre classification
music informationretrieval
Spectra
Studies
Support vector machines
SVM
Timbre
Valleys
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Summary:An automatic classification system of the music genres is proposed. Based on the timbre features such as mel-frequency cepstral coefficients, the spectro-temporal features are obtained to capture the temporal evolution and variation of the spectral characteristics of the music signal. Mean, variance, minimum, and maximum values of the timbre features are calculated. Modulation spectral flatness, crest, contrast, and valley are estimated for both original spectra and timbre-feature vectors. A support vector machine (SVM) is used as a classifier where an elaborated kernel function is defined. To reduce the computational complexity, an SVM ranker is applied for feature selection. Compared with the best algorithms submitted to the music information retrieval evaluation exchange (MIREX) contests, the proposed method provides higher accuracy at a lower feature dimension for the GTZAN and ISMIR2004 databases.
ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2012.6414994