Feature Space Modification for Content-Based Music Retrieval Based on User Preferences

This paper proposes a feature space modification method for feature extraction of music, which is effective for the development of a content-based music information retrieval (MIR) system based on user preferences. The proposed method conducts clustering of all songs in the music collection, and utilizes the resulting cluster IDs as training data for feature space modification, and is capable to automatically generate a feature space which is suitable to the content of any music collection. Experiment results prove that the proposed method improves accuracy of user preference based MIR