A new two‐microphone Gauss‐Seidel pseudo affine projection algorithm for speech quality enhancement

Djendi, Mohamed

doi:10.1002/acs.2754

Cited by 8 publications

(6 citation statements)

References 43 publications

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“…Hence this enables a representation of two-input two-output mixing system as shown in Eqn. (7). These mixing coefficients culminated to a square matrix which has the attribute of a full-rank matrix provided that 1, ( ) ≠ 2, ( ).…”

Section: A Auxiliary Channelmentioning

confidence: 99%

“…In the work 6 , a method is proposed to decorrelate multiple non-stationary stochastic sources using a multivariable crosstalk-resistant adaptive noise canceller. In related method 7 , the problem of speech quality enhancement is tackled using adaptive and non-adaptive filtering algorithms. A two-microphone Gauss-Seidel pseudo affine projection algorithm combined with forward blind source separation is proposed.…”

Section: Introductionmentioning

confidence: 99%

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Single channel informed signal separation using artificial‐stereophonic mixtures and exemplar‐guided matrix factor deconvolution

Al-Tmeme

Woo

Dlay

et al. 2018

Adaptive Control & Signal

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In this paper, a method is proposed to tackle the problem of single channel audio separation. The proposed method leverages on the exemplar source is used to emulate the targeted speech signal. A multi-component nonnegative matrix factor 2-D deconvolution (NMF2D) is proposed to model the temporal and spectral changes, and the number of spectral basis of the audio signals. The paper proposes an artificial auxiliary channel to imitate a pair of stereo mixture signals which is termed as "artificial-stereophonic mixtures". The artificial-stereophonic mixtures and the exemplar source are jointly used to guide the factorization process of the NMF2D. The factorization is adapted under a hybrid framework that combines the Generalized Expectation-Maximization algorithm with multiplicative update adaptation. The proposed algorithm leads to fast and stable convergence, and ensures the non-negativity constraints of the solution are satisfied. Adaptive sparsity has also been introduced on each sparse parameter in the multi-component NMF2D model when the exemplar deviates from the target signal. Experimental results have shown the competence of the proposed algorithms in comparison with other algorithms.

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Section: A Auxiliary Channelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single channel informed signal separation using artificial‐stereophonic mixtures and exemplar‐guided matrix factor deconvolution

Al-Tmeme

Woo

Dlay

et al. 2018

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…1,2 To break through this limitation, microphone array based speech enhancement techniques have emerged. [3][4][5][6][7] The microphone array consists of some microphones arranged in a geometric topology and its enhancement performance can be significantly improved by taking advantage of temporal spectral and spatial information about the speech and noise sources. However, conventional microphone array usually considers a relatively small number of microphones with fixed and regular locations, and these microphones cannot be flexibly changed, which prevents it from being widely applied in practice.…”

Section: Introductionmentioning

confidence: 99%

Distributed multichannel Wiener filtering for speech enhancement in acoustic sensor networks

Chang

Chen

Yin

2022

Adaptive Control & Signal

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Summary The performance of speech processing algorithms in wireless acoustic sensor networks (WASNs) is severely degraded by diffuse noise. To address this problem, a distributed speech enhancement algorithm for WASNs is proposed in this article. The parameterized multichannel Wiener filter is first improved by exploiting all cross‐correlation information among nodes. Next, using the Hadamard element‐wise product of label matrix and cross‐correlation matrix, the distributed parameterized multichannel Wiener filter based on the in‐network summation is presented. Moreover, by making the tradeoff between communication cost and enhancement performance, a simplified distributed algorithm is proposed. Finally, the noise reduction performance of the proposed method is theoretically analyzed. The proposed method achieves good speech enhancement performance and requires only in‐network summation in WASNs. Experimental results reveal the validity of the proposed method.

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“…The most popular adaptive filtering algorithms like the least mean square (LMS) and the normalized LMS (NLMS) algorithms are robust and have a low computational complexity [7,8]. The adaptive filters updates of LMS and NLMS algorithms are directly controlled by the input vector [9,10]. This property makes them very limited in terms of performances with a non-stationary signal like speech, but, in the other hand, very convenient for dispersive-impulse response type systems [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

New symmetric decorrelating set-membership NLMS adaptive algorithms for blind speech intelligibility enhancement

Djendi

Cheffi

2019

SN Appl. Sci.

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This paper addresses the problem of speech enhancement and acoustic noise reduction by partial and set-membership adaptive algorithms combined with the symmetric decorrelating adaptive (SAD) algorithm structure. In this paper, we propose two new adaptive algorithms based on set-membership principle that improve the original set-membership algorithm behavior in speech enhancement applications. The first proposed algorithm (called Proposed 1), is based on the combination of the SAD algorithm structure with a smart control that uses decorrelating properties between the output and the mixing signal to control and update the SAD adaptive filters. The second proposed algorithm (called Proposed 2) is a modification of Proposed 1 and based on a new regularization relations of the SAD adaptive filters that use a combination between the variance of the mixing and the output signals of the SAD structures. These two proposed algorithms (Proposed 1 and Proposed 2) aim to improve the convergence speed performance and the output signal-tonoise-ratio of the original SAD algorithm when no smart control of the adaptive filters is used. The proposed algorithms have very interesting properties with non-stationary signal like speech when the SAD algorithm is, used alone, fails. The simulation results that are obtained by the comparison between the proposed algorithms (Proposed 1 and Proposed 2) and the original two-channel set-membership NLMS algorithm have shown the best performances of Proposed 1 and Proposed 2 in terms of the following criteria: systems mismatch, segmental SNR and segmental men square error.

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A new two‐microphone Gauss‐Seidel pseudo affine projection algorithm for speech quality enhancement

Cited by 8 publications

References 43 publications

Single channel informed signal separation using artificial‐stereophonic mixtures and exemplar‐guided matrix factor deconvolution

Single channel informed signal separation using artificial‐stereophonic mixtures and exemplar‐guided matrix factor deconvolution

Distributed multichannel Wiener filtering for speech enhancement in acoustic sensor networks

New symmetric decorrelating set-membership NLMS adaptive algorithms for blind speech intelligibility enhancement

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