Polyphonic Instrument Identification Using Independent Subspace Analysis

CCRMA, Stanford University
Stanford, CA94305, USA
pj97@ccrma.stanford.edu

Abstract:

A system which tries to identify the musical instruments playing concurrently in a mixture is investigated in this paper. The features used in classification are derived from the Independent Subspace Analysis (ISA) which somewhat decomposes each source, and the mixture, into its statistically ``independent'' components. Without re-grouping or actually separating the sources, they offer physiologically-motivated classification of instruments, assuming the decomposition is robust to the mixing process. The system is evaluated on two-tonal instrument mixtures from a set of five instruments and a phrase of real song from CD.