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SIAM Journal on Scientific Computing
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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Machine Learning
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IEEE Transactions on Audio, Speech, and Language Processing
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IEEE Transactions on Audio, Speech, and Language Processing
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IEEE Transactions on Audio, Speech, and Language Processing
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IEEE Transactions on Multimedia
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IEEE Transactions on Multimedia
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Proceedings of the 20th ACM international conference on Multimedia
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This paper concerns the development of a music codebook for summarizing local feature descriptors computed over time. Comparing to a holistic representation, this text-like representation better captures the rich and time-varying information of music. We systematically compare a number of existing codebook generation techniques and also propose a new one that incorporates labeled data in the dictionary learning process. Several aspects of the encoding system such as local feature extraction and codeword encoding are also analyzed. Our result demonstrates the superiority of sparsity-enforced dictionary learning over conventional VQ-based or exemplar-based methods. With the new supervised dictionary learning algorithm and the optimal settings inferred from the performance study, we achieve state-of-the-art accuracy of music genre classification using just the log-power spectrogram as the local feature descriptor. The classification accuracies for benchmark datasets GTZAN and IS-MIR2004Genre are 84.7% and 90.8%, respectively.