Speech Enhancement based on Wiener Filter and Compressive Sensing (original) (raw)

Improvement of wiener filter based speech enhancement using compressive sensing

2014 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 2014

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 algorithms to adjust filter coefficients and produce an output signal that satisfies some statistical criterion. The objective measures 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 r...

Single Channel Speech Enhancement using Wiener Filter and Compressive Sensing

International Journal of Electrical and Computer Engineering (IJECE), 2017

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 Using Wiener Filter Based on Voiced Speech Probability

2020

In this digitized world, quality, accuracy and adaptability are more emphasized. Due to immense practical applications, desire for clean signal is highly essential at the user end. In this work, speech signal is considered for enhancement. For this, Wiener filter is proposed based on voiced speech probability (VSP). The probability of the speech signal depends on the performance of voice activity detection (VAD). The decision directed method with likelihood ratio test estimates the noise which improves the performance of VAD. After finding the speech probability, the noise is updated and estimated. The mean square error is optimized by Wiener filter, and the signal is enhanced. For verification and comparison, signal-to-noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) are considered. This proposed method can be utilized in real-time applications.

Speech Enhancement Techniques using Wiener Filter and Subspace Filter

In the speech enhancement method by using the wiener filter and subspace filter. Because of uses advantages in reduction in noise with the subspace speech enhancement technology and stable characteristics of the wiener filter. These proposed enhancements of speech method has a better performance. It can be removed colored noise from noisy speech signal. The proposed enhancement of multi-channel speech signal can be obtain a better speech recovery result as compared to the trandition multichannel wiener filter and the subspace filter.

Enhancing speech using an Adaptive Wiener Filter based algorithm

International Journal of Advances in Computing and Information Technology, 2012

Speech can be expressed as a mechanism of expressing thoughts and ideas using vocal sounds. In humans, speech signals are generated at vocal cords, travelled through the vocal tract, and finally produced & transmitted through speaker's mouth. These speech signals then usually travels through air or other mediums to the listener's ear, where it acts as pressure waves. The bandwidth of speech signal is around 4 KHz. The noise produced by various ambient sources such as vehicles normally lies in this frequency range. Therefore, speech signals get easily distorted by the ambient noise. These distorted or degraded speech signals are called noisy speech signals. This paper focuses on speech processing (in particularly speech enhancement) of the noisy speech signals. This is very important as speech is the most commonly used way of communication and interaction between humans; however, it is very complex to understand. Therefore, this paper proposes an adaptive algorithm based on Wiener filter for speech enhancement. The proposed adaptive Wiener filter depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics (mean and variance). The adaptive Wiener filter is implemented in time domain rather than in frequency domain. This is done to accommodate the random speech signal. The proposed method is compared to the traditional Wiener filter and the spectral subtraction methods.

Speech enhancement with an adaptive Wiener filter

International Journal of Speech Technology, 2013

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time

Single Channel Speech Enhancement: Using Wiener Filtering with Recursive Noise Estimation

Procedia Computer Science, 2016

This paper discusses the problem of single channel speech enhancement in stationary environments, and proposes Wiener filtering with the recursive noise estimation algorithm. The Wiener filter is a linear estimator and minimizes the mean-squared error between the original and enhanced speech. The algorithm is implemented in the frequency domain and depends on the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. For the noise estimation, the recursive noise estimation approach is used. In this approach, the noise estimation is done by past and present spectral power values, using a smoothing parameter. The value of smoothing parameter is selected in between [0 1]. For the performance evaluation of the proposed speech enhancement algorithm objective evaluations with informal listening tests are conducted for the speech sentences, pronounced by male and female speakers from the NOIZEUS corpus, degraded by White as well as Pink noise types at different SNR levels. For objective measures, signal to noise ratio, segmental signal to noise ratio, and the perceptual evaluation of speech quality are used. The measures prove that the speech enhanced by proposed algorithm is more pleasant to the human ear for both noise conditions in comparison to the conventional speech enhancement method.

Speech Enhancement in Presence of Noise using Spectral Subtraction and Wiener Filter

2015

The process of suppressing the back ground noise in speech signals can be improved by subtraction of an estimate of the average noise spectrum from the noisy signal spectrum. The main objective of this paper is to implement and evaluate speech enhancement techniques based on spectral subtraction methods in presence of noise. The two techniques discussed in this paper are spectral subtraction filter and wiener filter. The simulation results reveal the superiority of the proposed methods for different back ground noises. KeywordsSpectral Subtraction, Additive white Gaussian Noise, Weiner Filter, Power spectrum.

Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering

EUSIPCO, 2018

Supervised non-negative matrix factorization (NMF) is effective in speech enhancement through training spectral models of speech and noise signals. However, the enhancement quality reduces when the models are trained on data that is not highly relevant to a speech signal and a noise signal in a noisy observation. In this paper, we propose to train a classifier in order to overcome such poor characterization of the signals through the trained models. The main idea is to decompose the noisy observation into parts and the enhanced signal is reconstructed by combining the less-corrupted ones which are identified in the cepstral domain using the trained classifier. We apply unsupervised NMF followed by Wiener filtering for the decomposition, and use a support vector machine trained on the mel-frequency cepstral coefficients of the parts of training speech and noise signals for the classification. The results show the effectiveness of the proposed method compared with the supervised NMF.

Study of the widely linear Wiener filter for noise reduction

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010

This paper develops a new widely linear noise-reduction Wiener filter based on the variance and pseudo-variance of the short-time Fourier transform coefficients of speech signals. We show that this new noise-reduction filter has many interesting properties, including but not limited to: 1) it causes less speech distortion as compared to the classical noise-reduction Wiener filter; 2) its minimum meansquared error (MSE) is smaller than that of the classical Wiener filter; 3) it can increase the subband signal-to-noise ratio (SNR), while the classical Wiener filter has no effect on the subband SNR for any given signal frame and subband.