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- Single Channel Speech Enhancement using Wiener Filter and Compressive Sensing
- Speech Enhancement: Theory and Practice, Second Edition
- Single Channel Speech Enhancement using Wiener Filter and Compressive Sensing

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To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Download Free PDF. Teddy Gunawan. Download PDF. A short summary of this paper. The challenges focus on corrupted speech solution Revised May 31, between noise reduction and signal distortion. We used a modified Wiener Accepted Jun 14, 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 Keyword: spectrum and improve mitigation of interested signals. The CS is then Compressive sensing applied using the gradient projection for sparse reconstruction GPSR PESQ technique as a study system to empirically investigate the interactive effects PESQ improvement of the corrupted noise and obtain better perceptual improvement aspects to SNR listener fatigue with noiseless reduction conditions.

The proposed algorithm Speech enhancement shows an enhancement in testing performance evaluation of objective assessment tests outperform compared to other conventional algorithms at Wiener filter 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.

All rights reserved. Email: tsgunawan iium. Today innovation technologies, signal processing is one of the most powerful sources for modern designed engineering that capable of realizing various applications in their real implementations from theoretical aspect point of view to counterpart in different application areas.

There are always a trade- off between noise reduction and signal distortion. Most of research found that more noise reduction is always accompanied by more signal distortion [1], [2]. The main challenge of the speech enhancement process is to design effective algorithms to suppress the noise without introducing any possibility of perceptual distortion into the speech signal [1], [3].

Research and investigations on speech enhancement problem have been growing at a rapid rate that cover a broad spectrum of constrains, application, and issues. The challenging work for enhancing noisy speech is on single microphone and the speech problem that was degraded by the noise and remains widely open for investigation [3], [4].

Such problem is well known as single-channel speech enhancement and considered as the most difficult task [1], [5]. Most of the speech enhancement techniques have concentrated principally on statistically uncorrelated and independent additive noise [3], [5]. However, the design of effective algorithms that can combat additive noise while producing high quality and improved speech signal is limited. Thus, the studies of additive noise in various types of applications and their related behavior are crucial endeavors.

Most of the literatures focus on the difference of the noise sources in terms of temporal and spectral characteristics, and the range of the noise levels that may be encountered in real life [1]. Many existing researches on speech enhancement have based relatively on samples of speech quality measurements which has made it impossible to carry out satisfactory studies. This aspect of study may suggest a better understanding of the related characteristics with a great number of the noisy speech date available for the speech at various dB SNR environments [1].

Concerns have been expressed about speech enhancement approaches. However, there has been a few researches so far that seek possible solution to the speech enhancement based on compressive sensing CS technique. Consequently, the question remain whether it can achieve suitable high improvement in both its performance and quality. Thus, it may be useful to investigate and analyze this new approach of data acquisition which is known as compressive sensing CS technique [6].

In turn, new type of sampling theory can predict from the sparce signals and be constructed from what previously believed to be incomplete information [6]. This method also provides efficient algorithm which can be used for perfect recovery of the sparse signal [9].

Majority of researches in the CS techniques have been introduced in image processing to provide compressed version of the original image with noiseless distortion [6, 9]. This technique relies mainly on empirical observation that many signals can be well-approximated by sparse expression in terms of suitable basis [6].

In real processing speech enhancement techniques, the algorithm employed a simple principle in which the spectrum of the clean speech estimation signal can be obtained by subtracting a noise estimation spectrum from the noisy speech spectrum conditions. In general, speech enhancement [1], [12] was contaminated and degraded with additive noise. It is typically attacked by the background noise of uncorrelated speech.

It is often computed on a frame-by-frame basis. The noisy speech is then calculated in the discrete time domain of the short-time Fourier transform STFT in which it is generally non-stationary in nature.

For simplicity, the k term throughout the assumption of a frame segment are dropped. Equation 4 is not taken into action when the background noise is stationary and coverage to optimal estimate of noise power spectrum.

To synthesise results, the enhanced speech signal needs reconstruction. This phase is done by using the noisy phase as the clean speech estimation signal, due to insensitivity of the human auditory system [5], [13]. In extensive studied [16] and [17] reported that the gain improvement relatively used the parameters i. These variation parameter can be described as the free parameter and can be described as follows: a.

In [5], it is mentioned the advantages of the spectral subtraction algorithms as follow; 1 simple and only requiring noise estimation spectrum, and 2 variation of subtraction parameters with highly flexibility. Normally, it employs voice activity detection VAD in the form of statistical information of silence region. However, difficulty emerged when background noise is nonstationary.

Their shortcoming perceptually contains the remnant of unnatural noticeable to spectral artifacts known as musical noise in random frequencies. Its spectral floor parameters are used to prevent the cause of spectrum floor from going to below the preset minimum level rather that setting to zero. This algorithm depends on a posteriori segmental SNR and over subtraction factor can be calculate from Equation 8 [5]. In this technique will uniform the noise effects spectrum to the speech and predict the subtraction factors that was subtracted noisy by over-estimate of noise factor spectrum.

This parameter is to avoid the trade of the amount of remnant noise and the level of perceived musical noise. As such, the algorithm can reduce the level of perceived remnant musical noise while the remaining of the background noise is presented and distorted the enhanced speech signal. Many type of research also reported using other domain, e. It differs from the spectral subtraction by decomposing the noisy speech with Kahunen-Loeve-Transform KLT into subspace that occupied primarily by the clean speech vector space signal and noise vector space signal.

It is then estimated the signal the signal of interest and noise subspace from a subspace of the noisy Euclidean space [1]. In [1] mentioned that there are several different types of the spectral subtraction algorithms family. Accordingly, this spectral subtraction type estimates the speech by subtracting noise estimation from the noise speech or by multiplying the noise spectrum with gain functions, and then combine it with the phase of noisy speech.

Some of its examples, in briefly, are spectral over-subtraction, spectral subtraction based on perceptual properties, iterative spectral subtraction, multi-band spectral subtraction, Wiener filtering. Therefore, spectral subtraction types essentially were based on intuitive and heuristically based principles.

In Wiener filter type algorithms, the general idea is to minimize the mean square error criterion and to achieve the optimal filter as mention in [1], [18].

These constant referred as parametric Wiener filters in which to obtain their characteristic for speech solution. The enhanced speech estimation and its gain function is shown in Equation 9. This gain function is largely depend on the power spectrum density of the noise at a certain frequency that attenuates each frequency component. In [1], [11] reviewed the statistical model based algorithm.

Its method is justified by the statistics of speech and noise that are not available and there is no knowledge of the best distortion measure in the perception sense by modification of using Hidden Markov Model HMM based enhancement [1]. In general, this method adapted a composite source model by choosing a finite set of statistically independent Gaussian subsources model. This finite set is consider as switch that controlled by a Markov chain.

The limitation os the HMM-based system require a training phase to obtain the speech and noise models. It relatively increase the computational requirement. Moreover, [1] adapted with a non-causal estimator for a priori SNR and a corresponding non-causal to enhance speech signal. Besides that, other speech enhancement techniques [1], [4] also introduced. In [16] mentioned the modification of boosting techniques and its adaptation to temporal masking threshold of the human auditory system.

This masking threshold depends on human auditory system that typically using in speech and audio coding to lower the bitrate requirement. The gain function was depended on the global forward masking threshold and forward masking threshold in each subband [16]. It acted as the filter operation that expressed in time domain in order to evaluate the noise effects to the speech signal in each subband.

This speech enhancement algorithm is designed based on Wiener filter and compressive sensing CS. Noisy Spectrum and Update of Noise Estimate As shown in Figure 1, the speech signal has been contaminated by noise and it is well-known as noisy speech. With this method, the noisy speech is separate into a frame of 20 milliseconds in which each frame is corresponded to sample per frame by using the sampling rate of 8 kHz.

Let noisy speech y n as the input signal in term of time domain that consist of the clean speech s n and additive noise d n of independent source respectively. The equations are restated and simplified in order to make understandable. From Equation 1 and 2 , noise estimate [20] with the hypothesis formula can be expressed in Equation The noise estimation will calculate based on frame-by-frame noise estimation of Equation This Wiener technique in Equation 13 was modified based on [18] to obtain the high amplitude spectrum weight estimate when applying Equation 12 to non-linear optimal gain function of Equation 13 and produced the enhanced speech signal.

This modified technique will reduce the mismatch weight of the interested signal. Then, the inverse FFT transformed is synthesis. It also derived under assumption that of a key parameter in the reduction of the noise and improving the speech distortion where the technique given a decision-directed method as low computational load for real time operation. The proposed algorithm based on Wiener filter and compressive sensing technique 3. This novel CS approach is fundamentally different from the well-known Shannon sampling theorem [6].

This technique used sampling theory that of selecting the interested signal and recover with almost exact signal reconstruction from noiseless observations [6], [9].

The major advantage of the CS is the recovery predictions of the signals from incomplete measurements information that was applied in various applications. The CS method used gradient projection for sparse reconstruction GPSR to experimentally investigate the interactive effects of the corrupted noise and obtain better improvement to the listener with noiseless reduction [21].

This technique can be expressed as in Equation This CS modification technique relies on the key efficiency of the empirical observation with well sparse approximation in suitable basis by only small amount of nonzero coefficients [6], [9].

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Philipos C. Share; Like; Download. The book covers traditional speech enhancement algorithms, such as spectral subtraction and Loizou, Ph. Speech Enhancement: Theory and Practice

The purpose of the present work is to design robust estimators for speech enhancement by incorporation of calculation rank-order statistics and locally-adaptive neighborhoods. The proposed estimators are able to increase the speech quality of a noisy signal, to preserve better speech intelligibility, and to introduce less artifacts comparing with known speech enhancement estimators. We design a novel speech enhancement algorithm based on rank-order statistics and local adaptive signal processing to improve the accuracy of existing speech enhancement estimators, in terms of speech quality, intelligibility, and introduction of artificial artifacts. We found that by using the proposed estimators for speech enhancement we obtain a better adaptation to nonstationary characteristics of speech and noise processes comparing with that of known speech enhancement estimators.

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