Blind speech enhancement and acoustic noise reduction by SFTF adaptive algorithm

Henni, Rahima; Djebari, Mustapha; Djendi, Mohamed

doi:10.1109/icee-b.2017.8192120

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Equation (13) shows the non-linear score function of the fundamental IVA algorithm and is used for inter-frequency dependencies between source signals. However, the non-linear score function is not unique and is strongly dependent on the source prior.…”

Section: Proposed Multistage Bss Approachmentioning

confidence: 99%

“…Moreover, adaptive filtering with BSS can also reduce the noise, leading to speech enhancement and noise reduction. Forward Blind Source Separation (FBSS) combined with Simplified Fast transversal filters (SFTF) method results in adoption gain from forwarding prediction [13]. Nevertheless, adaptive filtering methods face problems while canceling or suppressing the acoustic noise.…”

Section: Introductionmentioning

confidence: 99%

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

Khan¹,

Jan²,

Khalil³

et al. 2021

Preprint

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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 of 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, 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%

Section: Introductionmentioning

confidence: 99%

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

Khan¹,

Jan²,

Khalil³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, adaptive filtering with BSS can also reduce the noise, leading to speech enhancement and noise reduction. Forward Blind Source Separation (FBSS) combined with the Simplified Fast transversal filter (SFTF) method results in adoption gain from forwarding prediction [13]. Nevertheless, adaptive filtering methods face problems while canceling or suppressing the acoustic noise.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…Similarly, the work of Rahima et al [15] used a joint approach based on blind signal separation and adaptive transversal filtering technique for the reduction of acoustic noise and speech enhancement. Lehmann et al [16] have designed a modified version of Edge receiver in order to reduce co-channel interference effect for the random linear modulation.…”

Section: Introductionmentioning

confidence: 99%

A novel and integrated architecture for identification and cancellation of noise from GSM signal

Rekha¹,

Jabeen

2019

IJECE

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There are multiple reasons for the evolution as well as the presence of noise over transmitted GSM signal. In spite of various approaches towards noise cancellation techniques, there are less applicable techniques for controlling noise in acoustic GSM signal. Therefore, the proposed manuscript presents an integrated modelling which performs modelling of noise identification that could significantly assist in successful noise cancellation. The proposed system uses three different approach viz. i) stochastic based approach for noise modelling, ii) analytical-based approach where allocated power acts as one of the prominent factors of noise, and iii) wavelet-based approach for effective decomposition of GSM signal for assisting better noise cancellation technique followed by better retention of signal quality. Simulated in MATLAB, the study outcome shows that it offers a cost-effective implementation, A Practical Approach for Noise identification, and Effective Noise Cancellation with Signal quality retention. The proposed system offers approximately 24% of enhancement in noise reduction as compared to any existing digital filters with 1.6 seconds faster in processing speed.

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Blind speech enhancement and acoustic noise reduction by SFTF adaptive algorithm

Cited by 5 publications

References 2 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

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

A novel and integrated architecture for identification and cancellation of noise from GSM signal

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