Hybrid Source Prior Based Independent Vector Analysis for Blind Separation of Speech Signals

Khan, Jawad; Jan, Tariqullah; Khalil, Ruhul Amin; Altalbe, Ali

doi:10.1109/access.2020.3010342

Cited by 11 publications

(12 citation statements)

References 31 publications

(116 reference statements)

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“…For example, [33] proposes a mixed source prior model comprised of Super-Gaussian and Student's T to enhance the performance of the BSS. Consequently, in [34], the performance is improved by using a hybrid model, consisting of multivariate super-Gaussian and generalized Gaussian source priors. This approach models the higher amplitudes of the observed convolutive mixture by multivariate generalized Gaussian source prior, and the low amplitude are exploited by multivariate Gaussian source prior.…”

Section: Contributionsmentioning

confidence: 99%

“…The generalized Gaussian model exploits higher-order statistical properties while multivariate Super-Gaussian models other related information in the hybrid source prior model approach. The weights of the source priors in the hybrid model are adopted following the energy components of the received convolutive mixture [34]. In the de-noising stage, the MMSE filtering method is used to suppress the noisy component in the received convolutive mixture signal.…”

Section: Proposed Multistage Bss Approachmentioning

confidence: 99%

“…Therefore, a hybrid model is proposed containing multivariate generalized Gaussian and Super-Gaussian source priors. The hybrid source prior model can better model low and higher amplitudes [34]. The Super-Gaussian source prior function models low energy amplitude, and the high energy amplitude is modeled by multivariate generalized Gaussian source prior.…”

Section: Proposed Multistage Bss Approachmentioning

confidence: 99%

See 2 more Smart Citations

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

Khan¹,

Jan²,

Khalil³

et al. 2021

Preprint

Self Cite

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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.

show abstract

Section: Contributionsmentioning

confidence: 99%

Section: Proposed Multistage Bss Approachmentioning

confidence: 99%

Section: Proposed Multistage Bss Approachmentioning

confidence: 99%

See 1 more Smart Citation

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

Khan¹,

Jan²,

Khalil³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, [33] proposes a mixed source prior model comprised of super-Gaussian and Student's T to enhance the performance of the BSS. Consequently, in [34], the performance is improved by using a hybrid model, consisting of multivariate super-Gaussian and generalized Gaussian source priors. This approach models the higher amplitudes of the observed convolutive mixture by a multivariate generalized Gaussian source prior, and the low amplitudes are exploited by a multivariate Gaussian source prior.…”

Section: Contributionsmentioning

confidence: 99%

“…The generalized Gaussian model exploits higher-order statistical properties while multivariate super-Gaussian models use other related information in the hybrid source prior model approach. The weights of the source priors in the hybrid model are adopted following the energy components of the received convolutive mixture [34]. In the de-noising stage, the MMSE filtering method is used to suppress the noisy component in the received convolutive mixture signal.…”

Section: Proposed Multistage Bss Approachmentioning

confidence: 99%

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

et al. 2021

Self Cite

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

Speech dereverberation and source separation using DNN-WPE and LWPR-PCA

Sheeja¹,

Sankaragomathi²

2023

Neural Comput & Applic

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Hybrid Source Prior Based Independent Vector Analysis for Blind Separation of Speech Signals

Cited by 11 publications

References 31 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

Speech dereverberation and source separation using DNN-WPE and LWPR-PCA

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