Title :
Polyphonic instrument identification using independent subspace analysis
Author_Institution :
CCRMA, Stanford Univ., CA
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 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 a real song from CD
Keywords :
audio signal processing; independent component analysis; musical instruments; signal classification; ISA; concurrently playing musical instruments; independent subspace analysis; musical instrument sound mixture; physiologically-motivated instrument classification; polyphonic instrument identification; song phrase; sound source identification; source/mixture decomposition; statistically independent components; two-tonal instrument mixtures; Clustering algorithms; Cost function; Humans; Independent component analysis; Instruction sets; Instruments; Robustness; Signal processing; Spectrogram; Time domain analysis;
Conference_Titel :
Multimedia and Expo, 2004. ICME '04. 2004 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
0-7803-8603-5
DOI :
10.1109/ICME.2004.1394439