Speech Enhancement Using MMSE Estimation and Spectral Subtraction Methods

Gupta, V. K.; Bhowmick, Anirban; Chandra, Mahesh; Sharan, Shambhu

doi:10.1109/icdecom.2011.5738532

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…Artificial noisy signals were prepared by adding car noise, machine gun noise and LYNX noise from NOISEX-92database [15] [4] [17] to this clean sentence at 0dB, 5dB, 10dB and 20dB SNR levels.…”

Section: A Preparation Of Noisy Databasementioning

confidence: 99%

Performance evaluation of front end speech enhancement techniques

Bhowmick

Chandra

Biswas

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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In this paper the performance of speech enhancement algorithms was evaluated and compared in terms of improvement in Signal-to-Noise Ratio (SNR) and speech quality. Clean and noisy version of one Continuous Hindi speech sentence was used for performing the experiments. Noisy version is obtained by mixing F16 noise, operations room noise and machine gun noise to clean speech signal at different SNR levels. The study of this paper leads to the utility of speech enhancement algorithms for different noises based on their characteristics.

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Section: A Preparation Of Noisy Databasementioning

confidence: 99%

Performance evaluation of front end speech enhancement techniques

Bhowmick

Chandra

Biswas

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

Self Cite

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“…Different solutions are provided to enhance the intelligibility and quality of the speech signals and improve the performance of the BSS systems. The classical techniques overcome this problem by using adaptive techniques like minimum means square error (MMSE) [1][2][3][4][5][6][7]. The MMSE adjusts itself according to the observed convolutive mixture.…”

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.

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“…Different solutions are provided to enhance the intelligibility and quality of the speech signals and improve the performance of the BSS systems. The classical techniques overcome this problem by using adaptive techniques such as minimum means square error (MMSE) [1][2][3][4][5][6][7]. The MMSE adjusts itself according to the observed convolutive mixture.…”

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

Speech Enhancement Using MMSE Estimation and Spectral Subtraction Methods

Cited by 12 publications

References 20 publications

Performance evaluation of front end speech enhancement techniques

Performance evaluation of front end speech enhancement techniques

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

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