Convolutive Speech Bases and Their Application to Supervised Speech Separation

Author

Smaragdis, Paris

Author_Institution

Mitsubishi Electr. Res. Labs., Cambridge, MA

Volume

15

Issue

1

fYear

2007

Firstpage

1

Lastpage

12

Abstract

In this paper, we present a convolutive basis decomposition method and its application on simultaneous speakers separation from monophonic recordings. The model we propose is a convolutive version of the nonnegative matrix factorization algorithm. Due to the nonnegativity constraint this type of coding is very well suited for intuitively and efficiently representing magnitude spectra. We present results that reveal the nature of these basis functions and we introduce their utility in separating monophonic mixtures of known speakers

Keywords

convolution; matrix decomposition; speech coding; convolutive basis decomposition method; convolutive speech; magnitude spectra; monophonic recordings; nonnegative matrix factorization algorithm; nonnegativity constraint; speakers separation; supervised speech separation; Context modeling; Higher order statistics; Independent component analysis; Matrix decomposition; Principal component analysis; Singular value decomposition; Source separation; Speech; Supervised learning; Unsupervised learning; Convolutive bases; nonnegative matrix factorization; source separation;

fLanguage

English

Journal_Title

Audio, Speech, and Language Processing, IEEE Transactions on

Publisher

ieee

ISSN

1558-7916

Type

jour

DOI

10.1109/TASL.2006.876726

Filename

4032795