PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation
Mikkel Schmidt and Morten Mørup
Independent Component Analysis and Blind Signal Separation, 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006, Proceedings Volume 3889, pp. 700-707, 2006.

Abstract

We present a novel method for blind separation of instruments in polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding to individual instruments. Based on this factorization we separate the instruments using spectrogram masking. The proposed algorithm has applications in computational auditory scene analysis, music information retrieval, and automatic music transcription.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:2721
Deposited By:Mikkel Schmidt
Deposited On:22 November 2006