I had a paper accepted with an oral presentation at this year’s ISMIR held in Kobe, Japan. The paper is called Steerable Playlist Generation by Learning Song Similarity from Radio Station Playlists and is co-authored with Eck, Desjardins and Lamere. It outlines two new ideas:
- Using commercial radio station playlists to learn a similarity space from audio features
- Use a steerable tag cloud to allow the user to influence the playlist generation
Here is the abstract:
This paper presents an approach to generating steerable playlists. We first demonstrate a method for learning song transition probabilities from audio features extracted from songs played in professional radio station playlists. We then show that by using this learnt similarity function as a prior, we are able to generate steerable playlists by choosing the next song to play not simply based on that prior, but on a tag cloud that the user is able to manipulate to ex- press the high-level characteristics of the music he wishes to listen to.