Robust MMSE filtering for single-microphone speech enhancement

Enzner, Gerald; Thune, Philipp

doi:10.1109/icassp.2017.7952909

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…After taking the derivative of the objective function in (14) and setting it equal to zero, we get (16) and (17)…”

Section: Improved Mask Threshold Constrained Wiener Filtermentioning

confidence: 99%

“…For the MMSE algorithm, it performs non-linear estimation of the speech spectral magnitude to minimise the MSE based on statistical models. In MMSE, the speech and noise discrete Fourier transform (DFT) coefficients can be obtained based on various statistical models, such as the Gaussian distribution model [3,15,16] and non-Gaussian distribution models [17][18][19][20] to achieve better enhanced speech quality.…”

Section: Introductionmentioning

confidence: 99%

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Probability decision‐driven speech enhancement algorithm based on human acoustic perception

Zhang

Wang

Idrees

et al. 2020

IET signal process.

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In this study, a novel human acoustic perception motivated Wiener filter speech enhancement system is presented to cope with real-world interfering background noises. Guiding by the speech presence probability, two alternative methods are proposed to reduce the noise by adopting the audible sound pressure level (SPL) and the masking characteristic of the human auditory system to achieve better listening comfort level. More specifically, when the probability of speech presence in the noisy signal is less than the decision threshold, a new SPL compressed method effectively reduces the noise. When the speech presence probability is more than the decision threshold, an improved acoustical mask threshold constrained Wiener filter approach enhances the noisy speech. Moreover, in order to evaluate the performance of the new system, the proposed algorithm is compared with the classic prior signal-to-noise ratio-based Wiener filter and three acoustic perception related algorithms. The experimental results show that the proposed algorithm significantly outperforms the four comparing algorithms in terms of speech quality and intelligibility either in stationary or moderate non-stationary noisy environments. Thus, the intended approach can be employed as the front-end module for various speech-related applications.

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“…After taking the derivative of the objective function in (14) and setting it equal to zero, we get (16) and (17)…”

Section: Improved Mask Threshold Constrained Wiener Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probability decision‐driven speech enhancement algorithm based on human acoustic perception

Zhang

Wang

Idrees

et al. 2020

IET signal process.

View full text Add to dashboard Cite

show abstract

“…The oldest spectral gain function is the suppression rule of the Wienertype filter [10], which tries to minimize the mean-square error criterion for speech enhancement. Other commonly used spectral gain-based approaches are the minimum mean square error-based (MMSE) estimator [11] and log minimum mean square error (log-MMSE) estimator [12], all of which have been proposed to implement the short-time spectral amplitude estimation of the speech signal. These MMSE methods are based on statistical models, such as the Gaussian distribution model [13] and non-Gaussian distribution models [14].…”

Section: Introductionmentioning

confidence: 99%

Teacher-Student Training Approach Using an Adaptive Gain Mask for LSTM-Based Speech Enhancement in the Airborne Noise Environment

Huang

2023

Chinese J. Elect.

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Research on speech enhancement algorithms in the airborne environment is of great significance to the security of airborne systems. Recently, the research focus of speech enhancement has turned from conventional unsupervised algorithms, like the log minimum mean square error estimator (log-MMSE), to the state-ofthe-art masking-based long short-term memory (LSTM) method. However, each method has its characteristics and limitations, so they cannot always handle noise well. Besides, the requirements of clean speech and noise data for training a supervised speech enhancement model are difficult to satisfy in the real-world airborne environment. Therefore, in this work, to fully utilize the advantages of those two different methods without any data restrictions, we propose a novel adaptive gain mask (AGM) based teacher-student training approach for speech enhancement. In our method, the AGM, as a robust learning target for the student model, is devised by incorporating the estimated ideal ratio mask from the teacher model into the procedure of the log-MMSE approach. To get an appropriate tradeoff between the two methods, we adaptively update the AGM using a recursive weighting coefficient. Experiments on the real airborne data show that the proposed AGM-based method outperforms other baselines in terms of all essential objective metrics evaluated in this paper.

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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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Robust MMSE filtering for single-microphone speech enhancement

Cited by 10 publications

References 23 publications

Probability decision‐driven speech enhancement algorithm based on human acoustic perception

Probability decision‐driven speech enhancement algorithm based on human acoustic perception

Teacher-Student Training Approach Using an Adaptive Gain Mask for LSTM-Based Speech Enhancement in the Airborne Noise Environment

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

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