A new regularized forward blind source separation algorithm for automatic speech quality enhancement

Zoulikha, Meriem; Djendi, Mohamed

doi:10.1016/j.apacoust.2016.05.012

Cited by 22 publications

(5 citation statements)

References 26 publications

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“…In 2016 Meriem Zoulikhaet al [23] The proposed RFBSS calculation is contrasted with later and established speech improvement algorithms in various noisy conditions. This correlation was assessed regarding Cepstral Distance (CD), the system mismatch (SM) and the Segmental signal-tonoise proportion (SegSNR) criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

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2018

IJET

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Section: Literature Reviewmentioning

confidence: 99%

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2018

IJET

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“…Numerous adaptive techniques were proposed for speech enhancement application, we can find time domain algorithm, frequency domain adaptive algorithms [22][23][24][25][26] or adaptive spatial filtering methods [27,28] that frequently employ adaptive SVD methods in order to separate the speech signal space from the noisy one. Another direction of research combines the Blind Source Separation (BSS) methods with adaptive filtering algorithms for enhancing the speech signal and to cancel effectively the acoustic echo components [29][30][31][32]. This approach employs at least two microphones configuration in order to update the adaptive filtering algorithms.…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement Based on LWT and Artificial Neural Network and Using MMSE Estimate of Spectral Amplitude

Talbi¹,

Baazaoui²,

Bouhlel³

2021

Deep Learning Applications

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In this chapter, we will detail a new speech enhancement technique based on Lifting Wavelet Transform (LWT) and Artifitial Neural Network (ANN). This technique also uses the MMSE Estimate of Spectral Amplitude. It consists at the first step in applying the LWTto the noisy speech signal in order to obtain two noisy details coefficients, cD1 and cD2 and one approximation coefficient, cA2. After that, cD1 and cD2 are denoised by soft thresholding and for their thresholding, we need to use suitable thresholds, thrj,1≤j≤2. Those thresholds, thrj,1≤j≤2, are determined by using an Artificial Neural Network (ANN). The soft thresholding of those coefficients, cD1 and cD2, is performed in order to obtain two denoised coefficients, cDd1 and cDd2 . Then the denoising technique based on MMSE Estimate of Spectral Amplitude is applied to the noisy approximation cA2 in order to obtain a denoised coefficient, cAd2. Finally, the enhanced speech signal is obtained from the application of the inverse of LWT, LWT−1 to cDd1, cDd2 and cAd2. The performance of the proposed speech enhancement technique is justified by the computations of the Signal to Noise Ratio (SNR), Segmental SNR (SSNR) and Perceptual Evaluation of Speech Quality (PESQ).

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“…However, such systems often need to eliminate highly nonstationary sounds, such as extraneous speaker voices, in real time with little delay. For achieving this, real-time blind source separation (BSS) is promising [2,3]. BSS is a technique that separates individual source signals from microphone array inputs without any prior information about the source signals.…”

Section: Introductionmentioning

confidence: 99%

Low Latency Online Blind Source Separation Based on Joint Optimization with Blind Dereverberation

Ueda

Nakatani

Ikeshita

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a new low-latency online blind source separation (BSS) algorithm. Although algorithmic delay of a frequency domain online BSS can be reduced simply by shortening the shorttime Fourier transform (STFT) frame length, it degrades the source separation performance in the presence of reverberation. This paper proposes a method to solve this problem by integrating BSS with Weighted Prediction Error (WPE) based dereverberation. Although a simple cascade of online BSS after online WPE upgrades the separation performance, the overall optimality is not guaranteed. Instead, this paper extends a recently proposed batch processing algorithm that can jointly optimize dereverberation and separation so that it can perform online processing with low computational cost and little processing delay (< 12 ms). The results of a source separation experiment in a noisy car environment suggest that the proposed online method has better separation performance than the simple cascaded methods.

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A new regularized forward blind source separation algorithm for automatic speech quality enhancement

Cited by 22 publications

References 26 publications

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Speech Enhancement Based on LWT and Artificial Neural Network and Using MMSE Estimate of Spectral Amplitude

Low Latency Online Blind Source Separation Based on Joint Optimization with Blind Dereverberation

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