Hidden Markov Models for spectral similarity of songs

  • Arthur Flexer Elias Pampalk G
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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.

Author-supplied keywords

  • Hidden Markov Models
  • Music Information Retrieval
  • Spectral Similarity

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  • SGR: 84871992944
  • SCOPUS: 2-s2.0-84871992944
  • PUI: 368053865
  • ISBN: 9788474023183


  • Gerhard Widmer Arthur Flexer Elias Pampalk

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