Key distributions as musical fingerprints for similarity assessment

Abstract

This paper presents a pitch-based approach for creating musical fingerprints for similarity assessment. An effective measure for musical similarity impacts music indexing and classification in music retrieval systems. The proposed method creates key distributions from polyphonic music, and compares the key distributions of pairs of pieces, by calculating their correlation coefficient, to determine a degree of similarity between them. The proposed method assumes no knowledge of the time structure of the piece, nor does it require pieces to be the same length. We present results using this method to assess similarity among selected variations by Mozart. The results show that the correlation coefficients of pieces from the same set of variations are centered on 0.88 (with a standard deviation of 0.11), and that of pieces across different sets of variations are centered on 0.32 (with a standard deviation of 0.31).