A Music Similarity Function Based on the Centroid Model

Abstract

Music-similarity computation is an essential building block for the browsing, retrieval, and indexing of digital music archives. This paper proposes a music similarity function based on the centroid model, which divides the feature space into non-overlapping clusters for the efficient computation of the timber distance of two songs. We place particular emphasis on the centroid deviation as a feature for music-similarity computation. Experiments show that the centroid-model representation of the auditory features is promising for music-similarity computation.