Flexible Fusion of Relevance and Importance in Music Ranking

Due to the proliferation of audio files on the Web and in large digital music collections, the ranking of the retrieved music becomes an important issue in Music Information Retrieval. This paper proposes a music-ranking strategy that can identify and flexibly fuse music audio, ranging from similar and potentially serendipitous music to authoritative or mainstream music. The notions of similar and authoritative music are double-folded identified based on user preference data and acoustic features extracted from the audio. The music-ranking employs kernel functions, which can be user-controllable through an intuitive parameter tuning. A research prototype system that incorporates the ranking mechanism is developed, and a user study is conducted on its use. The results of the survey envisage users' satisfaction with respect to the proposed music-ranking strategy, and its real-world applicability.