Hidden markov models for spectral similarity of songs

Abstract

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.