Noise reduction in speech processing

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In the dynamic mode the aggressiveness of noise reduction depends on the noise level. In other words; the more noise that is present - the more it is reduced. This facilitates the cleaning of strong transient noises such as noise created by passing cars while preserving the highest speech quality in quiet environments. Alango Noise Suppression technology includes some unique features such as tone suppression and tone preservation.

Tone suppression allows reducing strong periodic tonal noises. Tone preservation allows keeping the dial tone, DTMF or other tonal signals that are not noises and must be preserved. In Gaussian noise , each pixel in the image will be changed from its original value by a usually small amount. A histogram, a plot of the amount of distortion of a pixel value against the frequency with which it occurs, shows a normal distribution of noise.

While other distributions are possible, the Gaussian normal distribution is usually a good model, due to the central limit theorem that says that the sum of different noises tends to approach a Gaussian distribution.

In either case, the noise at different pixels can be either correlated or uncorrelated; in many cases, noise values at different pixels are modeled as being independent and identically distributed , and hence uncorrelated. There are many noise reduction algorithms in image processing [28]. In selecting a noise reduction algorithm, one must weigh several factors:. In real-world photographs, the highest spatial-frequency detail consists mostly of variations in brightness "luminance detail" rather than variations in hue "chroma detail". Since any noise reduction algorithm should attempt to remove noise without sacrificing real detail from the scene photographed, one risks a greater loss of detail from luminance noise reduction than chroma noise reduction simply because most scenes have little high frequency chroma detail to begin with.


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In addition, most people find chroma noise in images more objectionable than luminance noise; the colored blobs are considered "digital-looking" and unnatural, compared to the grainy appearance of luminance noise that some compare to film grain. For these two reasons, most photographic noise reduction algorithms split the image detail into chroma and luminance components and apply more noise reduction to the former. Most dedicated noise-reduction computer software allows the user to control chroma and luminance noise reduction separately.

One method to remove noise is by convolving the original image with a mask that represents a low-pass filter or smoothing operation. For example, the Gaussian mask comprises elements determined by a Gaussian function. This convolution brings the value of each pixel into closer harmony with the values of its neighbors.

In general, a smoothing filter sets each pixel to the average value, or a weighted average, of itself and its nearby neighbors; the Gaussian filter is just one possible set of weights. Smoothing filters tend to blur an image, because pixel intensity values that are significantly higher or lower than the surrounding neighborhood would "smear" across the area.

US20070033020A1 - Estimation of noise in a speech signal - Google Patents

Because of this blurring, linear filters are seldom used in practice for noise reduction; they are, however, often used as the basis for nonlinear noise reduction filters. Another method for removing noise is to evolve the image under a smoothing partial differential equation similar to the heat equation , which is called anisotropic diffusion. With a spatially constant diffusion coefficient, this is equivalent to the heat equation or linear Gaussian filtering, but with a diffusion coefficient designed to detect edges, the noise can be removed without blurring the edges of the image.

Another approach for removing noise is based on non-local averaging of all the pixels in an image. In particular, the amount of weighting for a pixel is based on the degree of similarity between a small patch centered on that pixel and the small patch centered on the pixel being de-noised. A median filter is an example of a non-linear filter and, if properly designed, is very good at preserving image detail. To run a median filter:.

USA1 - Estimation of noise in a speech signal - Google Patents

A median filter is a rank-selection RS filter, a particularly harsh member of the family of rank-conditioned rank-selection RCRS filters; [29] a much milder member of that family, for example one that selects the closest of the neighboring values when a pixel's value is external in its neighborhood, and leaves it unchanged otherwise, is sometimes preferred, especially in photographic applications. Median and other RCRS filters are good at removing salt and pepper noise from an image, and also cause relatively little blurring of edges, and hence are often used in computer vision applications.

The main aim of an image denoising algorithm is to achieve both noise reduction and feature preservation. In this context, wavelet-based methods are of particular interest. In the wavelet domain, the noise is uniformly spread throughout coefficients while most of the image information is concentrated in a few large ones. To address these disadvantages, non-linear estimators based on Bayesian theory have been developed. In the Bayesian framework, it has been recognized that a successful denoising algorithm can achieve both noise reduction and feature preservation if it employs an accurate statistical description of the signal and noise components.

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Statistical methods for image denoising exist as well, though they are infrequently used as they are computationally demanding. For Gaussian noise , one can model the pixels in a greyscale image as auto-normally distributed, where each pixel's "true" greyscale value is normally distributed with mean equal to the average greyscale value of its neighboring pixels and a given variance.

One method of denoising that uses the auto-normal model uses the image data as a Bayesian prior and the auto-normal density as a likelihood function, with the resulting posterior distribution offering a mean or mode as a denoised image. A block-matching algorithm can be applied to group similar image fragments into overlapping macroblocks of identical size, stacks of similar macroblocks are then filtered together in the transform domain and each image fragment is finally restored to its original location using a weighted average of the overlapping pixels.

Shrinkage fields is a random field -based machine learning technique that brings performance comparable to that of Block-matching and 3D filtering yet requires much lower computational overhead such that it could be performed directly within embedded systems.

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Various deep learning approaches have been proposed to solve noise reduction and such image restoration tasks. Deep Image Prior is one such technique which makes use of convolutional neural network and is distinct in that it requires no prior training data. Most general purpose image and photo editing software will have one or more noise reduction functions median, blur, despeckle, etc. Special purpose noise reduction software programs include Neat Image , Noiseless , Noiseware , Noise Ninja , G'MIC through the -denoise command , and pnmnlfilt nonlinear filter found in the open source Netpbm tools.

ANF II: noise cancellation device

From Wikipedia, the free encyclopedia. For the reduction of a sound's volume, see soundproofing. For the noise reduction of machinery and products, see noise control. Noise reduction example. Example of noise reduction using Audacity with 0 dB , 5 dB, 12 dB, and 30 dB reduction, Hz frequency smoothing, and 0.

Main article: Anisotropic diffusion. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy.

Email Address. Sign In. Access provided by: anon Sign Out. A comparative study of noise reduction techniques for automatic speech recognition systems Abstract: Automatic Speech Recognition systems are greatly influenced by noise. Noise generated in environment or channel tends to degrade the performance of speech recognition systems.

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