Music Database Retrieval Based on Spectral Similarity

Abstract

We present an efficient algorithm to retrieve similar music pieces from an audio database. The algorithm tries to capture the intuitive notion of similarity perceived by human: two pieces are similar if they are fully or partially based on the same score, even if they are performed by different people or at different speed. Each audio file is pre-processed to identify local peaks in signal power. A spectral vector is extracted near each peak, and a list of such spectral vectors forms our intermediate representation of a music piece. A database of such intermediate representations is constructed, and two pieces are matched against each other based on a specially-defined distance function. Matching results are then filtered according to some linearity criteria to select the best result to a user query.