The University of Southampton
University of Southampton Institutional Repository

A speech enhancement algorithm based on a Chi MRF model of the speech STFT amplitudes

A speech enhancement algorithm based on a Chi MRF model of the speech STFT amplitudes
A speech enhancement algorithm based on a Chi MRF model of the speech STFT amplitudes
A speech enhancement algorithm that takes advantage of the time and frequency dependencies of speech signals is presented in this paper. The above dependencies are incorporated in the statistical model using concepts from the theory of Markov Random Fields. In particular, the speech short-time Fourier transform (STFT) amplitude samples are modeled with a novel Chi Markov Random Field prior, which is then used for the development of an estimator based on the Iterated Conditional Modes method. The novel prior is also coupled with a dasiaharmonicpsila neighborhood, which apart from the immediately adjacent samples on the time frequency plane, also considers samples which are one pitch frequency apart, so as to take advantage of the rich structure of the voiced speech time frames. Additionally, central to the development of the algorithm is the adaptive estimation of the weights that determine the interaction between neighboring samples, which allows the restoration of weak speech spectral components, while maintaining a low level of uniform residual noise. Results that illustrate the improvements achieved with the proposed algorithm, and a comparison with other established speech enhancement schemes are also given.
chi, gaussian, markov random fields, short-time fourier transform (stft) estimation, speech enhancement
1558-7916
1508-1517
Andrianakis, Y.
acdaeffe-e767-4ba6-bca6-97e0a57b5cad
White, Paul R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Andrianakis, Y.
acdaeffe-e767-4ba6-bca6-97e0a57b5cad
White, Paul R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba

Andrianakis, Y. and White, Paul R. (2009) A speech enhancement algorithm based on a Chi MRF model of the speech STFT amplitudes. IEEE Transactions on Audio, Speech and Language Processing, 17 (8), 1508-1517. (doi:10.1109/TASL.2009.2022199).

Record type: Article

Abstract

A speech enhancement algorithm that takes advantage of the time and frequency dependencies of speech signals is presented in this paper. The above dependencies are incorporated in the statistical model using concepts from the theory of Markov Random Fields. In particular, the speech short-time Fourier transform (STFT) amplitude samples are modeled with a novel Chi Markov Random Field prior, which is then used for the development of an estimator based on the Iterated Conditional Modes method. The novel prior is also coupled with a dasiaharmonicpsila neighborhood, which apart from the immediately adjacent samples on the time frequency plane, also considers samples which are one pitch frequency apart, so as to take advantage of the rich structure of the voiced speech time frames. Additionally, central to the development of the algorithm is the adaptive estimation of the weights that determine the interaction between neighboring samples, which allows the restoration of weak speech spectral components, while maintaining a low level of uniform residual noise. Results that illustrate the improvements achieved with the proposed algorithm, and a comparison with other established speech enhancement schemes are also given.

This record has no associated files available for download.

More information

e-pub ahead of print date: 5 May 2009
Published date: November 2009
Keywords: chi, gaussian, markov random fields, short-time fourier transform (stft) estimation, speech enhancement

Identifiers

Local EPrints ID: 79029
URI: http://eprints.soton.ac.uk/id/eprint/79029
ISSN: 1558-7916
PURE UUID: 4e32b0c3-83e7-4838-8b8b-e508e4fd45dd
ORCID for Paul R. White: ORCID iD orcid.org/0000-0002-4787-8713

Catalogue record

Date deposited: 15 Mar 2010
Last modified: 14 Mar 2024 02:34

Export record

Altmetrics

Contributors

Author: Y. Andrianakis
Author: Paul R. White ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×