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Technical Program

Paper Detail

Paper:	MLSP-P1.4
Session:	Blind Source Separation II
Time:	Tuesday, May 16, 14:00 - 16:00
Presentation:	Poster
Topic:	Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis
Title:	SOUND SOURCE SEPARATION USING SHIFTED NON-NEGATIVE TENSOR FACTORISATION
Authors:	Derry FitzGerald, Matt Cranitch, Cork Institute of Technology, Ireland; Eugene Coyle, Dublin Institute of Technology, Ireland
Abstract:	Recently, shifted Non-negative Matrix Factorisation was developed as a means of separating harmonic instruments from single channel mixtures. However, in many cases two or more channels are available, in which case it would be advantageous to have a multichannel version of the algorithm. To this end, a shifted Non-negative Tensor Factorisation algorithm is derived, which extends shifted Non-negative Matrix Factorisation to the multi-channel case. The use of this algorithm for multi-channel sound source separation of harmonic instruments is demonstrated. Further, it is shown that the algorithm can be used to perform Non-negative Tensor Deconvolution, a multi-channel version of Non-negative Matrix Deconvolution, to separate sound sources which have time evolving spectra from multi-channel signals.