An adaptive distance measure for similarity based playlist generation

Nowadays, a large part of all music ever recorded is digitally available and due to MP3 already ten thousands of songs can be carried around on a mobile device. Intelligent automatic song selection is more and more required alternatively to random selection or manual playlist generation. We propose a system, that generates playlists including songs similar to accepted ones, discarding songs similar to rejected ones, where similar refers to timbre. Additional adaptivity is achieved with a user-adaptive distance function which in our case requires modeling features separately. After a seed-song (which is the first accepted song) is given by the user, the distance function is used by a song selection strategy to select songs. Minimal user feedback is collected with a skip button that is pressed to directly jump to the next song and explicitly reject the current one while acceptance is implicitly given by listening to a song.