Dynamic Playlist Generation Based On Skipping Behavior

Common approaches to creating playlists are to randomly shuffle a collection (e.g. iPod shuffle) or manually select songs. In this paper we present and evaluate heuristics to adapt playlists automatically given a song to start with (seed song) and immediate user feedback

Dynamic Playlist Generation Based on Skipping Behaviour

Common approaches to creating playlists are to randomly shuffle a collection (e.g. iPod shuffle) or manually select songs. In this paper we present and evaluate heuristics to adapt playlists automatically given a song to start with (seed song) and immediate user feedback. Instead of rich metadata we use audio-based similarity. The user gives feedback by pressing a skip button if the user dislikes the current song. Songs similar to skipped songs are removed, while songs similar to accepted ones are added to the playlist. We evaluate the heuristics with hypothetical use cases. For each use case we assume a specific user behavior (e.g. the user always skips songs by a particular artist). Our results show that using audio similarity and simple heuristics it is possible to drastically reduce the number of necessary skips.

From sound to ”sense” via feature extraction and machine learning: Deriving high-level descriptors for characterising music

Research in intelligent music processing is experiencing an enormous boost these days due to the emergence of the new application and research field of Music Information Retrieval (MIR). The rapid growth of digital music collections and the concomitant shift of the music market towards digital music distribution urgently call for intelligent computational suppor