Hidden Markov Models for spectral similarity of songs

Arthur Flexer; Elias Pampalk; Gerhard Widmer
DAFx-2005 - Madrid
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.