Single channel speech enhancement using Wiener filter and compressive sensing

The speech enhancement algorithms are utilized to overcome multiple limitation factors in recent applications such as mobile phone and communication channel. The challenges focus on corrupted speech solution between noise reduction and signal distortion. We used a modified Wiener filter and compressive sensing (CS) to investigate and evaluate the improvement of speech quality. This new method adapted noise estimation and Wiener filter gain function in which to increase weight amplitude spectrum and improve mitigation of interested signals. The CS is then applied using the gradient projection for sparse reconstruction (GPSR) technique as a study system to empirically investigate the interactive effects of the corrupted noise and obtain better perceptual improvement aspects to listener fatigue with noiseless reduction conditions. The proposed algorithm shows an enhancement in testing performance evaluation of objective assessment tests outperform compared to other conventional algorithms at various noise type conditions of 0, 5, 10, 15 dB SNRs. Therefore, the proposed algorithm significantly achieved the speech quality improvement and efficiently obtained higher performance resulting in better noise reduction compare to other conventional algorithms

Speech enhancement in non-stationary noise using compressive sensing

This paper addresses the problem of single channel speech enhancement algorithm in non-stationary noise environment which is rather difficult compared to the stationary noise. We proposed a new speech enhancement algorithm based on compressive sensing. First, the noise average estimation and Wiener filter gain are calculated. Compressive sensing using GPSR technique is then incorporated by randomly selected the sparse signal of unconstrained problem with suitable basis and reconstruct the noiseless distortion to the enhanced speech. The performance is evaluated using PESQ score improvement. Our proposed algorithm shows better performance compared to other traditional algorithms across two non-stationary noises at various SNRs. On average, the PESQ improvement was 19.14% and 7.12% for exhibition and restaurant noises, respectively

Objective evaluation of speech enhancement using compressive sensing algorithm

Most of the accurate method for the speech enhancement design mainly focuses on quality and intelligibility to produce high performance level by using compression techniques. A novel speech enhancement algorithm using compressive sensing (CS) is different paradigm from compression technique with low-dimensional geometry for transmission or storage. The CS algorithm, can directly acquire compressed data signals and replace samples by more general measurements of the uniform rate digitization with signal sparsity model. Perceptual evaluation of speech quality (PESQ) is an objective evaluation of speech enhancement algorithm used to measure the enhanced speech quality. All provable good measurement, with random matrics in CS algorithm, can enhance speech signal. Objective evaluation on various dB SNR shows that the proposed algorithm exhibits better noise reduction ability over conventional approaches without obvious degradation of the speech signal quality

Speech enhancement based on compressive sensing algorithm

There are various methods, in performance of speech enhancement, have been proposed over the years. The accurate method for the speech enhancement design mainly focuses on quality and intelligibility. The method proposed with high performance level. A novel speech enhancement by using compressive sensing (CS) is a new paradigm of acquiring signals, fundamentally different from uniform rate digitization followed by compression, often used for transmission or storage. Using CS can reduce the number of degrees of freedom of a sparse/compressible signal by permitting only certain configurations of the large and zero/small coefficients, and structured sparsity models. Therefore, CS is significantly provides a way of reconstructing a compressed version of the speech in the original signal by taking only a small amount of linear and non-adaptive measurement. The performance of overall algorithms will be evaluated based on the speech quality by optimise using informal listening test and Perceptual Evaluation of Speech Quality (PESQ). Experimental results show that the CS algorithm perform very well in a wide range of speech test and being significantly given good performance for speech enhancement method with better noise suppression ability over conventional approaches without obvious degradation of speech quality

Improvement of wiener filter based speech enhancement using compressive sensing

Many researches have been addressed on design approach for speech enhancement. They are mainly focus on speech quality and intelligibility to produce high performance level of speech signal. Wiener filter is one of the adaptive filter algorithm to adjust filter coefficients and produce an output signal that satisfies some statistical criterion. The objective measure will optimize using informal listening test and Perceptual Evaluation of Speech Quality (PESQ). The cascaded design approach of the Wiener filter and compressive sensing (CS) algorithm with random matrices were applied to exhibit and produce the better results. Therefore, applying the speech signal to this algorithm design in terms of appropriate basis functions of relatively few nonzero coefficients in CS can achieve an optimal estimate of uncorrelated components of noisy speech without obvious degradation of speech quality. Aside from that, this algorithm can be promised the speech enhancement with high performance results and significantly improved comparing to classical methods

Speech enhancement based on wiener filter and compressive sensing

In the last few decades, many advanced technologies have been proposed, in which communications played a great role as well as telecommunications applications. The noise elimination in various environments became the most concerned as it greatly hindered the speech communication applications. The improvement of noisy speech interms of quality and intelligibility are taken into account without introducingany additional noise. Many speech enhancement algorithms have been proposed. Wiener filter is one of the classical algorithm that improve the noisy speech by reducing its noise components through selectively chosen Wiener gain. In this paper, compressive sensing method by randomize measurement matrix is combined with the Wiener filter to reduce the noisy speech signal to produce high signal to noise ratio. The PESQ is used to measure the quality of the proposed algorithm design. Experimental results showthe effectiveness of our proposed algorithm to enhance noisy signals corrupted by various noises compared to other traditional algorithms, in which high PESQ scores were achieved across various noises and different SNRs

Single Channel Speech Enhancement using Wiener Filter and Compressive Sensing

The speech enhancement algorithms are utilized to overcome multiple limitation factors in recent applications such as mobile phone and communication channel. The challenges focus on corrupted speech solution between noise reduction and signal distortion. We used a modified Wiener filter and compressive sensing (CS) to investigate and evaluate the improvement of speech quality. This new method adapted noise estimation and Wiener filter gain function in which to increase weight amplitude spectrum and improve mitigation of interested signals. The CS is then applied using the gradient projection for sparse reconstruction (GPSR) technique as a study system to empirically investigate the interactive effects of the corrupted noise and obtain better perceptual improvement aspects to listener fatigue with noiseless reduction conditions. The proposed algorithm shows an enhancement in testing performance evaluation of objective assessment tests outperform compared to other conventional algorithms at various noise type conditions of 0, 5, 10, 15 dB SNRs. Therefore, the proposed algorithm significantly achieved the speech quality improvement and efficiently obtained higher performance resulting in better noise reduction compare to other conventional algorithms.

Development of low bit rate speech encoder based on vector quantization and compressive sensing

Speech coding is a representation of a digitized speech signal using as few bits as possible, while maintaining reasonable level of speech quality. Due to growing need for bandwidth conservation in wireless communication, the research in speech coding has increased. Recently, Compressive Sensing (CS) is gaining a great interest because of its ability to recover original signals by taking only few measurements. CS is a new approach that goes against the common data acquisition methods. In this research, a new system of speech encoding system is developed using compressive sensing. Since CS performs well in sparse signals, different sparsifying transforms are analyzed and compared using Gini coefficient. The quality of the speech coder is evaluated using Perceptual Evaluation of Speech Quality (PESQ), Signal-to-Noise Ratio (SNR) and subjective listening tests. Results show that the speech coders have achieved a PESQ score of 3.16 at 4 kbps which is a good quality as confirmed by listening tests. Furthermore, the coder is also compared with Code Excited Linear Prediction (CELP) coder