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Single channel speech music separation using nonnegative matrix factorization and spectral masks

Grais, EM and Erdogan, H (2011) Single channel speech music separation using nonnegative matrix factorization and spectral masks

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Abstract

A single channel speech-music separation algorithm based on nonnegative matrix factorization (NMF) with spectral masks is proposed in this work. The proposed algorithm uses training data of speech and music signals with nonnegative matrix factorization followed by masking to separate the mixed signal. In the training stage, NMF uses the training data to train a set of basis vectors for each source. These bases are trained using NMF in the magnitude spectrum domain. After observing the mixed signal, NMF is used to decompose its magnitude spectra into a linear combination of the trained bases for both sources. The decomposition results are used to build a mask, which explains the contribution of each source in the mixed signal. Experimental results show that using masks after NMF improves the separation process even when calculating NMF with fewer iterations, which yields a faster separation process. © 2011 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Grais, EMUNSPECIFIEDUNSPECIFIED
Erdogan, HUNSPECIFIEDUNSPECIFIED
Date : 29 September 2011
Identification Number : https://doi.org/10.1109/ICDSP.2011.6004924
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:54
Last Modified : 17 May 2017 13:54
URI: http://epubs.surrey.ac.uk/id/eprint/840746

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