Thresholding wavelet-based forward BSS algorithm for speech enhancement and complexity reduction

Ghribi, Khadidja; Djendi, Mohamed; Berkani, Daoud

doi:10.1109/icnlsp.2018.8374376

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…; φ < 0.5 (17) where φ = [0, 1] is the weighting parameter, which depends on the normalized energy of the received noisy convolutive mixture. The weights of the non-linear score functions and φ are adjusted by the normalized energy of the mixture at every frequency block.…”

Section: Proposed Multistage Bss Approachmentioning

confidence: 99%

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An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture

et al. 2021

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This paper proposes a novel efficient multistage algorithm to extract source speech signals from a noisy convolutive mixture. The proposed approach comprises two stages named Blind Source Separation (BSS) and de-noising. A hybrid source prior model separates the source signals from the noisy reverberant mixture in the BSS stage. Moreover, we model the low- and high-energy components by generalized multivariate Gaussian and super-Gaussian models, respectively. We use Minimum Mean Square Error (MMSE) to reduce noise in the noisy convolutive mixture signal in the de-noising stage. Furthermore, the two proposed models investigate the performance gain. In the first model, the speech signal is separated from the observed noisy convolutive mixture in the BSS stage, followed by suppression of noise in the estimated source signals in the de-noising module. In the second approach, the noise is reduced using the MMSE filtering technique in the received noisy convolutive mixture at the de-noising stage, followed by separation of source signals from the de-noised reverberant mixture at the BSS stage. We evaluate the performance of the proposed scheme in terms of signal-to-distortion ratio (SDR) with respect to other well-known multistage BSS methods. The results show the superior performance of the proposed algorithm over the other state-of-the-art methods.

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Section: Proposed Multistage Bss Approachmentioning

confidence: 99%

“…The estimated source signal enhancement in the presence of acoustic noise is performed by Threshold Wavelet-based Forward Blind Source Separation (TWFBSS). This approach reduces the computational complexity from the Wavelet-based Forward Blind Source Separation (WFBSS) method [17].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture

et al. 2021

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“…Following its tremendous success for image denoising [27]− [30], the wavelet thresholding has made inroads into speech enhancement [31]− [41], which is hereafter also referred to as speech denoising. This was mainly motivated by advantages offered by the wavelet transform (WT) over the STFT.…”

Section: Introductionmentioning

confidence: 99%

“…To provide improved extraction of speech from noise, the Teager energy (TE) operator was also utilized in [31], [37], and [38]. With the use of a two microphone system, the blind source separation (BSS) technique was employed to separate the speech and noise wavelet coefficients in [40], [41]. To reduce speech distortion, various custom threshold functions with continuous derivative characteristic were proposed in [36]− [41] to replace the hard/soft threshold function.…”

Section: Introductionmentioning

confidence: 99%

“…This is in contrast to the need for special psychoacoustic or perceptual wavelets in [35]- [38] and more sophisticated custom threshold functions by the subband-level thresholding algorithms in [32], [33], [35], [37], and [38] to avoid excessive distortion in the enhanced speech. • Unlike its BSS counterparts in [40], [41], the proposed adaptive wavelet thresholding algorithm requires only one single microphone. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Performance Improvement of Adaptive Wavelet Thresholding for Speech Enhancement Using Generalized Gaussian Priors and Frame-Wise Context Modeling

2020

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This work aims at developing an adaptive wavelet thresholding algorithm for speech enhancement with significant performance improvement over other wavelet-based counterparts. This is accomplished through the formulation of the optimum threshold for noise reduction, based on the generalized Gaussian priors to fully characterize the statistics of speech and noise wavelet coefficients. In addition, through the frame-wise context modeling which enables tracking of the statistical characteristics of each individual coefficient on the frame-wise basis, the optimum threshold is accurate and adaptive at both the coefficient level and frame level. The frame-wise context model is formulated by virtue of the context subspace projection of the wavelet coefficients, with the context index employed as the invariant correspondence between successive frame parameters, thereby enabling the frame-wise tracking at the coefficient level. Simulation results show significant improvement over the wavelet-based speech enhancement algorithms in terms of the segmental signal-to-noise ratio improvement by as much as 226%, the perceptual evaluation of speech quality by 36%, the short-time objective intelligibility by 17.8% and the cepstral distance by 33.3%. When benchmarked with the well-established short-time-Fouriertransform-based counterparts, the proposed wavelet thresholding algorithm offers favorable and more robust performances, particularly under non-stationary noise conditions, with no adverse musical noise effect.INDEX TERMS context modeling, speech enhancement, wavelet thresholding.

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Implementation of Speech Enhancement Using Bionic Wavelet Transform

Bhagya

Ashwini²,

Bharathi³

2023

Information and Communication Technology for Competitive Strategies (ICTCS 2022)

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Thresholding wavelet-based forward BSS algorithm for speech enhancement and complexity reduction

Cited by 3 publications

References 8 publications

An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture

An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture

Performance Improvement of Adaptive Wavelet Thresholding for Speech Enhancement Using Generalized Gaussian Priors and Frame-Wise Context Modeling

Implementation of Speech Enhancement Using Bionic Wavelet Transform

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