A new way to enhance speech signal based on compressed sensing

Haneche, Houria; Boudraa, Bachir; Ouahabi, Abdeldjalil

doi:10.1016/j.measurement.2019.107117

Cited by 53 publications

(29 citation statements)

References 38 publications

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“…The higher the score the better the enhancing effect. We make a comparison with a state-of-the-art speech enhancement algorithm [ 24 ], which we refer to as the reference method. The undersampling ratio is set as 0.9 for both methods.…”

Section: Resultsmentioning

confidence: 99%

Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior

Jia

Liu

Jiang

2020

Sensors

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By means of compressive sampling (CS), a sparse signal can be efficiently recovered from its far fewer samples than that required by the Nyquist–Shannon sampling theorem. However, recovering a speech signal from its CS samples is a challenging problem, as it is not sparse enough on any existing canonical basis. To solve this problem, we propose a method which combines the approximate message passing (AMP) and Markov chain that exploits the dependence between the modified discrete cosine transform (MDCT) coefficients of a speech signal. To reconstruct the speech signal from CS samples, a turbo framework, which alternately iterates AMP and belief propagation along the Markov chain, is utilized. In addtion, a constrain is set to the turbo iteration to prevent the new method from divergence. Extensive experiments show that, compared to other traditional CS methods, the new method achieves a higher signal-to-noise ratio, and a higher perceptual evaluation of speech quality (PESQ) score. At the same time, it maintaines a better similarity of the energy distribution to the original speech spectrogram. The new method also achieves a comparable speech enhancement effect to the state-of-the-art method.

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Section: Resultsmentioning

confidence: 99%

Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior

Jia

Liu

Jiang

2020

Sensors

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“…The better quality of speech signal is obtained when PESQ score is higher. It is evident from the Table 1 that, when the frequency‐sensitive detail is taken into consideration, the spectral structure of the temporal continuity of speech is possibly secured, leading to the improvement of the PESQ for the proposed method when compared with References 1‐3, BIRMP and OGAM of Reference 40.…”

Section: Resultsmentioning

confidence: 99%

“…So, enhancement of speech signal is required. 1 Noise is the major concern in the signal processing applications which makes the detection of the reference signals difficult. 2 Therefore, the noise suppression is the major challenge in various research endeavors in the speech processing field over the last few generations.…”

Section: Introductionmentioning

confidence: 99%

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A novel double pole transfer function‐single frequency filtering approach for speech enhancement

Srinivasarao

Ghanekar

2020

Trans Emerging Tel Tech

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Speech intelligibility improvement is a major field of recent research with in audiology and hearing domain. Speech signal usually gets affected due to various environmental and background noise. Several approaches for speech intelligibility improvement were presented in the literature. In this paper, a method of speech enhancement based on Double Pole Transfer Function-Single Frequency Filter (DPTF-SFF) has been proposed. In the SFF approach the temporal and spectral resolutions are controlled with a one particular value of the filter that corresponds to the pole position in the complex z-plane. The proposed method uses double pole transfer function SFF that improves the magnitude and phase for various frequencies which are used in the synthesis of the original signal. In the analysis of this method clean speech signals are used from GRID corpus database and noise signals are taken from NOIZEUS and NOISEX-92 databases. The performance evaluation of this method is carried out in terms of Perceptual Evaluation of Speech Quality, Short-Time Objective Intelligibility, and Segmental Signal to Noise Ratio metrics. The implementation results proved that the proposed method is an efficient one as it gives higher intelligibility scores compared to the existing methods at different SNR levels (dB) and for different noises employed.

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“…Researchers, engineers and practitioners in various technical fields such as multimedia [25], telecommunications [26][27][28], medicine and biology [7,29,30], crack tracking and fracture detection [8,9,31,32,33], fluid mechanics [34], thermodynamics [35], astrophysics [36], finance [37,38], etc., are daily confronted with increasingly challenging technological problems at multiple scales of analysis, in terms of classification, segmentation, detection (of contours or parameters of interest), noise reduction or even elimination [39], compression for transmission or storage, synthesis or reconstruction, etc.…”

Section: Multiresolution Analysis Based On Waveletsmentioning

confidence: 99%

Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis

Arbaoui¹,

Ouahabi²,

Jacques³

et al. 2021

Preprint

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In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original procedure allows to reach a high accuracy close to 0.90. In this work, we have also implemented another approach for automatic detection of external cracks by deep learning from publicly available datasets.

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A new way to enhance speech signal based on compressed sensing

Cited by 53 publications

References 38 publications

Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior

Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior

A novel double pole transfer function‐single frequency filtering approach for speech enhancement

Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis

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