Impulse noise

About: Impulse noise is a research topic. Over the lifetime, 4816 publications have been published within this topic receiving 63970 citations.

The interaction of whole body vibration and impulse noise

Abstract: Three groups of chinchillas, each consisting of five monaural animals, were exposed to one of three conditions: 1 h of sinusoidal, 30‐Hz vibration at 1 g rms; 50 noise impulses at 155 dB, 1.5‐m A duration, at the rate of 1/min; or a combination of the vibration and the impulse noise. Before exposure, and at various times after exposure, each animal’s auditory evoked responses (AER) were measured at seven frequencies between 0l5 and 8.0 kHz. Thirty days after the exposure all animals were sacrificed for cochlear surface preparation histology. Chinchillas exposed to the vibration alone showed no significant temporary or permanent change in AER thresholds. The group exposed to impulse noise showed a maximum median TTS of from 34 dB at 0.5 kHz to 72 dB at 2 kHz and a flat PTS of 15–20 dB between 1 and 2.8 kHz. The combination group at all test times and frequencies showed a greater TTS and PTS than did the groups exposed to noise or vibration alone. The cochleograms are related in a variety of ways with hearing thresholds. The data from all three groups of experimental animals are consistent and demonstrate a potentiating effect of vibration on an impulse noise exposure.

Fast least mean M-estimate algorithms for robust adaptive filtering in impulse noise

Abstract: Adaptive filters with suitable nonlinear devices are very effective in suppressing the adverse effect due to impulse noise. In a previous work, the authors have proposed a new class of nonlinear adaptive filters using the concept of robust statistics [1,2]. The robust M-estimator is used as the objective function, instead of the mean square errors, to suppress the impulse noise. The optimal coefficient vector for such nonlinear filter is governed by a normal equation which can be solved by a recursive least squares like algorithm with O(N2) arithmetic complexity, where N is the length of the adaptive filter. In this paper, we generalize the robust statistic concept to least mean square (LMS) and transform domain LMS algorithms. The new fast nonlinear adaptive filtering algorithms called the least mean M-estimate (LMM) and transform domain LMM (TLMM) algorithms are derived. Simulation results show that they are robust to impulsive noise in the desired and input signals with an arithmetic complexity of order O(N).

Impulsive Noise Cancellation Based on Soft Decision and Recursion

Abstract: In this paper, we propose a new method to recover band-limited signals corrupted by impulsive noise. The proposed method successively uses adaptive thresholding and soft decisioning to find the locations and amplitudes of the impulses. In our proposed method, after estimating the positions and amplitudes of the additive impulsive noise, an adaptive algorithm, followed by soft decision, is employed to detect and attenuate the impulses. In the next step, by using an iterative method, an approximation of the signal is obtained. This signal approximation is successively used to improve the noise estimate. The algorithm is analyzed and verified by computer simulations. Simulation results confirm the robustness of the proposed algorithm, even if the impulsive noise exceeds the theoretical reconstruction capacity.

Impulse noise and hand-arm vibration in relation to sensory neural hearing loss.

Abstract: The present study was carried out to determine whether impulse noise and simultaneous exposure to noise and vibration can aggravate sensory neural hearing loss (SNHL) among forest (N = 199) and shipyard (N = 171) workers. The average level of exposure to noise outside the used earmuffs and the average exposure over time were nearly equal for the two groups. The impulsiveness of the noise and the average exposure level inside the earmuffs were measured with a miniature microphone. The hearing threshold of the workers was measured at 4 kHz and then estimated according to Robinson's model to compare the observed and expected hearing loss. The impulsiveness of the noise was greater both outside and inside the earmuffs in shipyard work than in forest work. The average SNHL was higher than predicted for the shipyard workers and about the same as predicted for the forest workers. The total exposure level inside the earmuffs was influenced by the total wearing time. The low frequencies of the chain-saw noise were not attenuated sufficiently by the earmuffs to protect the workers' hearing. The present study suggests that exposure to impulse noise increases the risk of SNHL, but that simultaneous exposure to hand-arm vibration and noise does not.

A Local Consensus Index Scheme for Random-Valued Impulse Noise Detection Systems

Abstract: The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.