Abstract: Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made about the nature or stationarity of the noise. No Voice Activity Detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equations whose solutions lead to the first estimates of speech and noise power spectra. Based on the selected noise model from the initial noise power spectrum estimate, the noise source type is identified and the input SNR estimated in this first step. The first power spectra estimates are then refined using approximate but, explicit MMSE and MAP estimation formulations. The refined estimates are then used in a Wiener filter to reduce noise and enhance the noisy speech. The proposed filtering schemes show good results. Nevertheless, it is shown that the MAP explicit solution, introduced here for the first time, reduces the computation time to less than one third in comparison to the MMSE solution. Slight higher improvements in SNR and PESQ score and less distortion are also noted.
OALibJ | DOI:10.4236/oalib.1102319 2 January 2016 | Volume 3 | e2319 bad solution image. Wavelet analysis  has good localization properties and the characteristics of multi-reso- lution analysis in time domain and frequency domain at the same time, and it can separate the signal and noise effectively, it satisfies the requirement of various de-noising such as low-pass, high-pass, random noise. Com- paring with the traditional de-noising method, it has the incomparable advantages and it becomes an important tool for signal analysis. Using wavelet transform for signal de-noising, it can ensure no damage to the signal at the same time of the de-noising. Based on the above analysis, an improved wavelet and Wiener filtering algo- rithm in image de-noising is put forward in this paper. The method makes full use of the phase invariability of the stationary wavelet transform, fully considering the correlation of wavelet coefficient, and it gets good effect on image de-noising processing.
Human face detection and recognition is a major topic for modern day researchers. It is very important in many computer fields like criminal identification, access and security, E- banking, Online shopping site, Net banking etc. Real time face detection and recognition is not a simple problem. Many approaches have been already implemented like template matching, neural network, MRC etc., Different algorithm have been used over the past few years. These algorithms have own some disadvantages. The techniques used in our paper are the most effective among those. The algorithms applied in this paper for face detecting and tracking is KLT algorithm and wiener filtering is applied for calculating matching percentage of different features points for recognition process.
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In the future, there will be a growing need for more flexible but eﬃcient utilization of radio resources. Increased flexibility in radio transmission, however, yields a higher likelihood of interference owing to limited coordination among users. In this paper, we address the problem of flexible spectrum sharing where a wideband single carrier modulated signal is spectrally overlapped by unknown narrowband interference (NBI) and where a cyclic Wiener filter is utilized for nonparametric NBI suppression at the receiver. The pulse shape design for the wideband signal is investigated to improve the NBI suppression capability of cyclic Wiener filtering. Specifically, two pulse shaping schemes, which outperform existing raised cosine pulse shaping schemes even for the same amount of excess bandwidth, are proposed. Based on computer simulation, the interference suppression capability of cyclic Wiener filtering is evaluated for both the proposed and existing pulse shaping schemes under several interference conditions and over both AWGN and Rayleigh fading channels.
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Abstract—This paper proposes the application of the Wiener ﬁlter in an adaptive manner in 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 to accommodate for the varying nature of the speech signal. The proposed method is compared to the traditional Wiener ﬁlter and the spectral subtraction methods and the results reveal its superiority.
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Although image denoising has been researched quite extensively, developing a denoising method that could remove noise effectively without eliminating the image fine details and edges is still a challenging task. Until recent years, many denoising methods have been proposed. Some recent non-linear methods, such as the adaptive Total Variation (ATV)  and the non-local means (NLM) , suggest employing different denoising approaches for the smooth and non-smooth regions. Conversely, linear methods such as the Wiener filter  balance the tradeoff between inverse filtering and noise smoothing by eliminating additive noise while inverting blurring.
A block diagram of the proposed SE algorithm is shown in Figure 1. The initial four frames are assumed to be noise only. The algorithm can be described as follows. The input noisy speech signal is decomposed into frames of 20-ms length with an overlap of 10 ms by the Hann window. Each segment was transformed using a 160-point discrete Fourier transform (DFT). The spectrum of the segmented noisy and noise signal are estimated by the multitaper method and then further refined by wavelet threshold- ing technique. The estimated ‘clean’ spectrum was gotten from the refined multitaper estimated noisy and noise spectrum. On the other hand, the noise-corrupted sen- tences were enhanced by the Wiener algorithm based on a priori SNR estimation . The region I+II constraints were then imposed on the enhanced spectrum. Finally, the inverse fast Fourier transform (FFT) was applied to obtain the enhanced speech signal.
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A BSTRACT - Speckle noise becomes a dominating factor in degrading the image visual quality and perception in medical images. Speckle noise is a particular kind of noise which affects all coherent imaging systems including medical ultrasound images and astronomical images. It is essential to keep the useful data in the exact original form. So we need to process the data by applying transformations. DWT (Discrete wavelet transform) is the latest and best technique for image denoising. This paper presents study of various techniques for removal of speckle noise from biomedical images such as Spatial and frequency domain filter and a modified algorithm for speckle noise reduction using wavelet based Multiresolutional analysis and combined filtering techniques with wiener and median filters. A comparative analysis of three methods: DWT with wiener filtering, DWT with median filtering and DWT with both wiener and median filtering techniques has been presented. Results are compared in terms of PSNR, Mean squared Error and processing time.
The bias, the consistency and the minimum attainable mean square estimation error of the estimator we propose are still unknown. However, the experimental results that are presented are very promising. First, when the Minimum- Probability-of-Error decision scheme for the non-coherent detection of modulated sinusoidal carriers in independent AWGN is tuned with the outcome of our estimator instead of the true value of the noise standard deviation, the Binary Error Rate tends to the optimal error probability when the number of observations is large enough. Second, given some speech signal corrupted by independent AWGN, our estimator can be used to estimate the noise standard deviation so as to adjust the standard Wiener filtering of the noisy speech. The objective performance measurements obtained by so proceeding are very close to those achieved when the Wiener filtering is tuned with the true value of the noise standard deviation.
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In this paper, we investigate theoretically the mecha- nism of bioradar for speech detection and develop a 35.5 GHz microwave bioradar which can detect speech at some distance by emitting radio waves in the microwave range to a target subject and extracting speech informa- tion from echo signal. In addition, in order to suppress the noise and improve the quality of the detected speech, we use spectral subtraction, and Wiener filtering algo- rithm respectively to enhance the bioradar speech and evaluate the performance of the two methods using spec- trogram.
ABSTRACT: The proposed work mainly focuses on reduction of EMG (Electromyogram) noise in ECG signal. The use of Wavelet Transform (WT) can be effective for suppressing EMG (muscle) noise compared to linear filtering as it provides information about both time and frequency characteristics simultaneously. The proposed algorithm reduces EMG noise using wavelet wiener filtering. Parameters of wiener filter are adapted according to the level of interference in the input signal. Important parameters used for adaptation are decomposition depth of input signal, thresholding method used, threshold size and filter banks. LMS (Least Mean Square) filtering of adaptively denoised ECG signal is also done to improve filtering performance. Testing is performed by taking ECG signal from standard MIT/BIH arrhythmia database. The proposed AWWF (Adaptive Wavelet Wiener Filtering) algorithm along with post LMS filtering provides better results by increasing SNR (Signal-to-Noise Ratio) and reducing mean square error.
In this paper, an adaptive low-rank channel estimation scheme based on the Wiener filtering (WF) technique is proposed for MB-OFDM UWB communication systems. This reduced-rank (RR) WF-based algorithm employs an adaptive 2-to-1 fuzzy-inference controlled (FIC) filter rank. It can be shown that the fuzzy-inference system (FIS)  offers an effective and robust means to monitor instantaneous fluctuations of a dense multipath channel and thus is able to assist the RR-WF-based channel esti- mator in selecting an appropriate time-varying filter rank p. As a result, the proposed RR-WF-based channel estimation possesses the potential to accomplish sub- stantial saving on computational complexity without affecting system bit-error-rate (BER) performance. To emphasize the importance of the use of an adaptive RR- WF scheme, both the MSE and the BER performances are evaluated and compared with the piecewise linear , the Gaussian second-order , the cubic-spline , the LS, and the fullrank WF channel estimation  algorithms. Simulation results have shown that the pro- posed FIC RR-WF scheme reduces successfully compu- tational complexity without sacrificing the BER performance under different UWB channel conditions.
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Polynomial J -spectral factorization arises in different ar- eas of systems and control, for example in the case of Wiener filtering, LQG theory, in the polynomial- and in the behavioral approach to H ∞ -control and filtering (see , , . Many algorithms have been suggested for the solution of such problem, especially in the case when n − = 0, i.e. J = I w (see , , , , , , ,
The inverse filtering is a restoration technique for de- convolution, i.e., when the image is blurred by a known lowpass filter, it is possible to recover the image by in- verse filtering or generalized inverse filtering. However, inverse filtering is very sensitive to additive noise. The approach of reducing one degradation at a time allows us to develop a restoration algorithm for each type of deg- radation and simply combine them. The Wiener filtering executes an optimal tradeoff between inverse filtering and noise smoothing. It removes the additive noise and inverts the blurring simultaneously. The Wiener filtering is optimal in terms of the mean square error. In other words, it minimizes the overall mean square error in the process of inverse filtering and noise smoothing. The Wiener filtering is a linear estimation of the original im- age. The approach is based on a stochastic framework .
We proposed key modifications of the block-matching and 3D-filtering algorithm, which were aimed at enhanc- ing the filter when applied to atomic-resolution electron micrographs of periodic crystals. We have shown that, through the proposed modifications, the denoising per- formance is significantly improved compared to the orig- inal BM3D on all tested images. It also substantially out- performs common linear filters such as median-filtering and low-pass Wiener filtering. The major advances are the adoption of a Fourier-based periodic similarity search  within the non-local means setting to the BM3D algorithm, as well as the treatment of an issue regarding spatial block concentration, which only occurs in the new BM3D setting. Furthermore, we showed that the proposed filter with its uniform adaptive periodic block match- ing, specifically tailored to perfect crystal structures, is able to significantly enhance both visually and quantita- tively the image quality of low-dose electron micrographs. Quantitative measures of interest to the material science community, namely atomic column detectability and posi- tion precision, are significantly improved by application of the new denoising algorithm, without the introduction of artifacts such as false-positive identification of atomic columns or shifts in the atomic column image positions beyond the sub-picometer level.
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The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles .In this paper we proposed a Curvelet Transformation based image denoising, which is combined with wiener filter in place of the low pass filtering in the transform domain. We demonstrated through simulations with images contaminated by three different noise i.e. Gaussian, salt and pepper and speckle. Experimental results show denoising of an image is done by processing an image through Wiener filter and using curvelet transform , . Experimental results show that proposed denoising technique performs better in terms of the PSNR. Simple de-noising algorithms that use the curvelet transform consist of three steps.
The de-noising of digital images is crucial preprocessing step before moving toward image segmentation, representation and object recognition. It is important to find out efficacy of filter for different noise models because filtering operation is application oriented task and performance varies according to type of noise present in images. A comparative study has made to elucidate the behavior of different spatial filtering techniques under different noise models. In this paper different types of noises like Gaussian noise, Speckle noise, Salt & Pepper noise are applied on grayscale standard image of Lenna and using spatial filtering techniques the values of full reference based image quality metrics are found and compared in tabular and graphical form. The outcome of comparative study shows that Lee, Kuan and Anisotropic Diffusion Filter worked well for Speckle noise, the Salt and Pepper noise has significantly reduced using Median and AWMF, and the Mean filter and Wiener filter works immensely efficient for reducing Gaussian noise. Keywords: spatial filter, additive noise, multiplicative noise, image quality metrics.
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In this study, there were various technique of filter used including Kaun filter, Wiener filter, Frost filter and Anisotropic Diffusion filter in order to reduce spackle noise of ultrasound pancreas images. Then, the filtered images were measured using Mean Square Error (MSE), Power Signal to Noise Ratio (PSNR), Average Difference (AD), Normalized Cross-Correlation (NCC), Maximum Difference (MD), Structural Content (SC), and Normalized Absolute Error (NAE) formula to evaluate the best quality images before undergo the segmentation process. As the result, Wiener filter was selected. In the segmentation process, the active countor method and level set method were evaluated. Then, the area of binary image and error percentage were calculated. As a conclusion, it shows that the filtering process using Wiener filter and segmentation method using level sets method has been successfully done to produce the best geometry of pancreas image.
Images captured by cameras may produce noise due to malfunction of camera pixels. And these captured images often were polluted by various noises during the course in which they are generated or transmitted. Due to which the quality of the image will be damaged. The disturbance is most likely the noise in the digital images and this is generated due to the transmission of an image. We have different types of noises like Gaussian noise, spike noise or impulse noise etc. Impulse noise is also called salt and pepper noise. The most well-known type of the noise in images is the salt and pepper noise. It is the noise, which, sprinkles on the images like white (salt) and black (pepper) dots significantly reduces the visual effects of images. Wiener filtering algorithm and median filtering algorithms, are the two most used algorithms, to remove impulse noise present in the digital image. But, the regular filtering algorithms are not effective to remove the noise, especially for high noise densities. To reduce the high level noise density and to improve the quality of image, it is very important to keep the margins (edges) and details, as well as, removing noises pixels in images.
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There are many approaches used in speckle reduction by previous researchers. Gabor filter, Wiener filter, Median filter, and Wavelet filter are types of commonly used filtering approaches by previous research. Wiener filter which is a statistical filter specially designed to suppress additive noise has some limitations in noise filtering. Median filter is a non-linear filter that will replace the original gray level by the median of the gray values of pixels in a specific neighbourhood . Wavelet filter is extensively used in noise reduction and data compression for the analysis of multi-scale structure on image.