O'Grady, Paul D. and Pearlmutter, Barak A. (2007) Discovering Convolutive Speech Phones using Sparseness and Non-Negativity Constraints. In: Proceedings of the Seventh International Conference on Independent Component Analysis, September 9-12, 2007, London, UK.

Download (127kB)

Abstract

Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness constraint. In combination with a spectral magnitude transform of speech, this method extracts speech phones (and their associated sparse activation patterns), which we use in a supervised separation scheme for monophonic mixtures.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Non-negative Matrix Factorisation (NMF); Convolutive NMF; Sparse Convolutive NMF.
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	1313
Depositing User:	Barak Pearlmutter
Date Deposited:	25 Mar 2009 17:20
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

Repository Staff Only(login required)

Item control page

Downloads

Origin of downloads