Filter & Denosing MATLAB Projects

Implementation of Butterworth, Chebyshev -I and Elliptic Filter for Speech Analysis

In this project, three kinds of infinite impulse response filter i.e. Butterworth, Chebyshev -I and Elliptical filter have been discussed theoretically and experimentally. Butterworth, Chebyshev type I and elliptic pass that is low high pass, band pass and musical organization stop filter have already been designed using MATLAB Software. The impulse responses, magnitude responses, phase responses of Butterworth, Chebyshev type I and Elliptical filter for filtering the speech signal have been observed.

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ADAPTIVE FILTERING USED IN HEARING AIDS OF IMPAIRED PEOPLE

An algorithm, which enhances speech by attenuating any kind of noise is proposed in this project. Right here, adaptive filter, which may adapt it self based on the character of sound is used. Further, primary differentiation that’s been made between speech and sound is the fact that message is extremely non-stationary in a period interval of 250ms whereas noise stationary. The algorithms are simulated with the aid of matlab program and also to implement using TMS320C6X DSP processor for real time application.

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Hybrid Median Filter design

The primary objective of this project would be to remove sound in optimum amount by preserving the image. Image processing consists of numerous filters in purchase to take away the impulse noises. Further, Hybrid median filter that is version that is somewhat improved of filter is explored. The hybrid removes the noise much better than median filter.

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DESIGN OF FIR FILTER USING SYMMETRIC STRUCTURE

In this project an area efficient method is proposed to design and implement FIR filter using MATLAB. The filter is implemented by utilizing equiripple window. Further, the proposed designs have been created and simulated using Matlab 7.0. It’s been shown that, the form that is direct FIR filter creates the same output as the Direct form FIR however the cost varies in terms hardware requirements of Direct kind Symmetric FIR. It has additionally been shown that, dynamically reconfigurable filters are efficiently implemented by using form that is direct FIR structure for equiripple FIR filter.

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Wavelet Signal and Image Denoising Using MATLAB

The paper deals with the use of wavelet transform for signal and image denoising employing a selected method of thresholding of appropriate decomposition coefficients. The proposed technique is based upon the analysis of wavelet transform and it includes description of global modification of its values. The whole method is verified for simulated signals and applied for processing of biomedical signals representing EEG signals and MR images corrupted by additional random noise.

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Wavelet Signal and Image Denoising using MATLAB

The paper deals with the use of wavelet transform for signal and image denoising employing a selected method of thresholding of appropriate decomposition coefficients. The proposed technique is based upon the analysis of wavelet transform and it includes description of global modification of its values. The whole method is verified for simulated signals and applied for processing of biomedical signals representing EEG signals and MR images corrupted by additional random noise.

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Application of Spatial Domain Filters on Noisy Images using MATLAB

Noise is an unwanted information present in an image. Such unwanted information in an image can be removed with filters. In digital image processing, filters can be applied on an image in two ways, which include spatial and frequency domain. This paper mainly deals with the application of spatial domain filters on noisy images for the purpose of identifying the efficiency of the filters in terms of enhancing the quality of the image by removing the noise present on it.

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Image Denoising Using New Adaptive based Median Filter Using MATLAB

Noise is a major issue while transferring images through all kinds of electronic communication. One of the most common noise in electronic communication is an impulse noise which is caused by unstable voltage. In this paper, the comparison of known image denoising techniques is discussed and a new technique using the decision based approach has been used for the removal of impulse noise. All these methods can primarily preserve image details while suppressing impulsive noise. The principle of these techniques is at first introduced and then analyzed with various simulation results using MATLAB. Most of the previously known techniques are applicable for the denoising of images corrupted with less noise density. Here a new decision based technique has been presented which shows better performances than those already being used. The comparisons are made based on visual appreciation and further quantitatively by Mean Square error (MSE) and Peak Signal to Noise Ratio (PSNR) of different filtered images.

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A general framework for denoising phaseless diffraction measurements Using MATLAB

We propose a general framework to recover underlying images from noisy phaseless diffraction measurements based on the alternating directional method of multipliers and the plug-and-play technique. The algorithm consists of three-step iterations: (i) Solving a generalized least square problem with the maximum a posteriori (MAP) estimate of the noise, (ii) Gaussian denoising and (iii) updating the multipliers. The denoising step utilizes higher order filters such as total generalized variation and nonlocal sparsity based filters including nonlocal mean (NLM) and Block-matching and 3D filtering (BM3D) filters. The multipliers are updated by a symmetric technique to increase convergence speed.

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Characterization of Auditory Evoked Potentials from Transient Binaural Beats Generated by Frequency Modulating Sound Stimuli Using MATLAB

When two pure-tone (2T) stimuli with slightly different frequencies are presented independently to each ear, an auditory illusion, called binaural beats (BB), is perceived as a faint pulsation over a single tone. The frequency of the perceived tone is equal to the mean frequency of 2T and the pulsation has a rate equal to the difference of the two. The interaction of the 2T stimuli, inside the auditory cortex, can be recorded in the form of auditory steady state responses (ASSR) using conventional electroencephalography (EEG) or magnetoencephalography (MEG). The recorded ASSR usually have small amplitudes and require additional signal processing to separate them from the surrounding cortical activity.

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Swept-Tone Evoked Otoacoustic Emissions: Stimulus Calibration and Equalization Using MATLAB

Otoacoustic Emissions (OAE) are minute acoustic responses originating from the cochlea as a result of an external acoustic stimulus and are recorded using a sensitive microphone placed in the ear canal. OAEs are acquired by synchronous stimulation with an acoustic click or tone burst and recording of the post-stimulus responses. This method of acquiring OAEs is known as transient evoked otoacoustic emissions (TEAOE) and is commonly used in clinics as a screening method for hearing and cochlear functionality in infants. Recently, a novel method of acquiring OAEs utilizing a swept-tone, or chirp, as a stimulus was developed. This method used a deconvolution process to compress the swept tone response into an impulse or click-like response.

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Acquisition of Otoacoustic Emissions Using Swept-Tone Techniques Using MATLAB

Otoacoustic emissions (OAEs) have been under investigation since their discovery 30 years ago (Kemp, 1978). Otoacoustic emissions are quiet sounds generated within the cochlea that can be detected with a sensitive microphone placed within the ear canal. They are used clinically as a hearing screening tool but have the potential for diagnostic and monitoring purposes. For this dissertation, high-resolution instrumentation was developed for improving the acquisition of OAEs. It was shown that a high bit-depth device is required in order to simultaneously characterize the ear canal and the cochlear responses. This led to a reduction in the stimulus artifact that revealed early latency, high-frequency otoacoustic emissions.

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