Blind source separation with perceptual post processing

Parikh, Devangi N.; Anderson, David V.

doi:10.1109/dsp-spe.2011.5739233

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Furthermore, in multiple audio sources with multiple microphones scenarios, the performance of the BSS separation process is improved by using the BSS output to generate the wiener filter coefficients and apply them to the desired speech signals [12]. Moreover, adaptive filtering with BSS can also reduce the noise, leading to speech enhancement and noise reduction.…”

Section: Introductionmentioning

confidence: 99%

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.

show abstract

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

“…Furthermore, in multiple audio sources with scenarios with multiple microphones, the performance of the BSS separation process is improved by using the BSS output to generate the Wiener filter coefficients and applying them to the desired speech signals [12]. Moreover, adaptive filtering with BSS can also reduce the noise, leading to speech enhancement and noise reduction.…”

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

“…Furthermore, it can be considered to extent ICA methods for 2D images in order to develop imaging methods in the diagnosis of temporomandibular joint disorders (Tvrdy, 2007). Besides, it could also be possible to further improve the TMD analysis performance assisted by a postprocessing scheme (Parikh, 2011). We would thus combine/fuse sound with the visual data such as those related to facial movement which are comfortable and safe to acquire, in order to further help the audio analysis to characterize TMD.…”

Section: Future Directionsmentioning

confidence: 99%