Download Hidden Markov Models for spectral similarity of songs 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.
Download Generating similarity-based playlists using travleling salesman algorithms When using a mobile music player en-route, usually only little attention can be paid to its handling. Nonetheless it is desirable that all music stored in the device can be accessed quickly, and that tracks played in a sequence should match up. In this paper, we present an approach to satisfy these constraints: a playlist containing all tracks stored in the music player is generated such that in average, consecutive pieces are maximally similar. This is achieved by applying a Traveling Salesman algorithm to the pieces, using timbral similarities as the distances. The generated playlist is linear and circular, thus the whole collection can easily be browsed with only one input wheel. When a chosen track finishes playing, the player advances to the consecutive tracks in the playlist, generally playing tracks similar to the chosen track. This behavior could be a favorable alternative to the wellknown shuffle function that most current devices – such as the iPod shuffle, for example – have. We evaluate the fitness of four different Traveling Salesman algorithms for this purpose. Evaluated aspects were runtime, the length of the resulting route, and the genre distribution entropy. We implemented a Java applet to demonstrate the application and its usability.