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OFAI-TR-2005-08 ( 82kB g-zipped PostScript file,  143kB PDF file)

Hidden Markov Models for spectral similarity of songs

Arthur Flexer, Elias Pampalk, Gerhard Widmer

Hidden Markov Models (HMM) are compared to Gaussian Mixture Models (GMM) for describing spectral similarity of songs. Contrary to previous work we make a direct comparison based on the log-likelihood of songs given an HMM or GMM. Whereas the direct comparison of log-likelihoods clearly favors HMMs, this advantage in terms of modeling power does not allow for any gain in genre classification accuracy.

Keywords: Hidden Markov Models, Spectral Similarity, Music Information Retrieval

Citation: Flexer A., Pampalk E., Widmer G.: Hidden Markov Models for spectral similarity of songs. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2005-08, 2005