Cluster aware normalization for enhancing audio similarity

An important task in Music Information Retrieval is content-based similarity retrieval in which given a query music track, a set of tracks that are similar in terms of musical content are retrieved. A variety of audio features that attempt to model different aspects of the music have been proposed. In most cases the resulting audio feature vector used to represent each music track is high dimensional. It has been observed that high dimensional music similarity spaces exhibit some anomalies: hubs which are tracks that are similar to many other tracks, and orphans which are tracks that are not similar to most other tracks. These anomalies are an artifact of the high dimensional representation rather than actually based on the musical content. In this work we describe a distance normalization method that is shown to reduce the number of hubs and orphans. It is based on post-processing the similarity matrix that encodes the pair-wise track similarities and utilizes clustering to adapt the distance normalization to the local structure of the feature space.