University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Fast dictionary learning for sparse representations of speech signals

Jafari, MG and Plumbley, MD (2011) Fast dictionary learning for sparse representations of speech signals IEEE Journal on Selected Topics in Signal Processing, 5 (5). pp. 1025-1031.

Fast dictionary learning for sparse representations of speech signals.pdf - Accepted version Manuscript
Available under License : See the attached licence file.

Download (823kB) | Preview
Text (licence)
Available under License : See the attached licence file.

Download (33kB) | Preview


For dictionary-based decompositions of certain types, it has been observed that there might be a link between sparsity in the dictionary and sparsity in the decomposition. Sparsity in the dictionary has also been associated with the derivation of fast and efficient dictionary learning algorithms. Therefore, in this paper we present a greedy adaptive dictionary learning algorithm that sets out to find sparse atoms for speech signals. The algorithm learns the dictionary atoms on data frames taken from a speech signal. It iteratively extracts the data frame with minimum sparsity index, and adds this to the dictionary matrix. The contribution of this atom to the data frames is then removed, and the process is repeated. The algorithm is found to yield a sparse signal decomposition, supporting the hypothesis of a link between sparsity in the decomposition and dictionary. The algorithm is applied to the problem of speech representation and speech denoising, and its performance is compared to other existing methods. The method is shown to find dictionary atoms that are sparser than their time-domain waveform, and also to result in a sparser speech representation. In the presence of noise, the algorithm is found to have similar performance to the well established principal component analysis. © 2011 IEEE.

Item Type: Article
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Jafari, MG
Plumbley, MD
Date : 1 September 2011
DOI : 10.1109/JSTSP.2011.2157892
Copyright Disclaimer : © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Adaptive dictionary, Dictionary learning, Sparse decomposition, Sparse dictionary, Speech analysis, Speech denoising.
Depositing User : Symplectic Elements
Date Deposited : 16 Aug 2016 09:28
Last Modified : 31 Oct 2017 18:34

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800