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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.

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.

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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: https://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: 06 Jun 2018 13:12

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Contributors

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

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