An efficient method for the removal of impulse noise from speech and audio signals

C, Chandra Sekhar; Moore, Michael S.; Mitra, S.K.

doi:10.1109/iscas.1998.698795

Cited by 33 publications

(16 citation statements)

References 2 publications

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“…For audio signals, several time-domain algorithms have been developed to detect and remove impulse noise. [1][2][3] However, these algorithms do not exploit the differences in spectral and temporal characteristics of speech and impulse noise to maximize the detection performance.…”

Section: Introductionmentioning

confidence: 99%

Impulse-noise suppression in speech using the stationary wavelet transform

Nongpiur

Shpak

2013

The Journal of the Acoustical Society of America

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An approach for detecting and removing impulse noise from speech using the wavelet transform is proposed. The approach utilizes the multi-resolution property of the wavelet transform, which provides finer time resolution at higher frequencies than the short-time Fourier transform to effectively identify and remove impulse noise. The paper then describes how the impulse-detection performance is dependent on certain wavelet features and their relationships with the impulse noise and the underlying speech signal. Performance comparisons carried out with an existing method show that the wavelet approach yields much better features for detecting the impulses. To remove the impulses, an algorithm that uses the stationary wavelet transform has been developed. The algorithm uses a two-step approach where the wavelet coefficients corresponding to the impulses are suppressed in the first step and then substituted by suitable coefficients located within the vicinity of the impulse in the second step. Performance evaluations with an existing method show that the proposed algorithm gives superior results.

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Section: Introductionmentioning

confidence: 99%

Impulse-noise suppression in speech using the stationary wavelet transform

Nongpiur

Shpak

2013

The Journal of the Acoustical Society of America

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“…ROWA filter was compared with standard noise filters. The comparison was made between the median, ROAD [Garnett et al, 2005], t-stats [Xiao et al, 2003], PA ], MSM [Chen & Wu, 2001], SDROM [Chandra et al, 1999], PCNN [Ma et al, 2003;Chartier & Renaud, 2006] and Tri-state [Chen et al, 1999]. To have an accurate estimate of the performance of each filter, Monte Carlo were performed, 100 noisy time series were generated per filter algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…Median filters proved to be robust to remove noise, but the signal loss must be lower than half the filter convolution window [Chandra et al, 1999]. Since in the original image the maximum length of noise was 50 data (50/60 = 0.83 sec.…”

Section: Eye Tracker Noisementioning

confidence: 99%

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An online noise filter for eye-tracker data recorded in a virtual environment

Chartier

Renaud

2008

Proceedings of the 2008 Symposium on Eye Tracking Research &Amp; Applications - ETRA '08

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“…However, this filter performs poorly when the signal is highly corrupted by impulse noise. Other OSF filter, such as the signal dependant rank ordered mean filter (SD-ROMF) [8,9,10] have also been successfully used for impulse noise removal. In this paper we present a filter based on fuzzy logic to remove impulse noise from 2D electrical resistivity data.…”

Section: Introductionmentioning

confidence: 99%

Impulse noise reduction in 2D electrical resistivity imaging data based on fuzzy logic

Ferahtia

Djarfour

Baddari

et al. 2011

2011 International Conference on Communications, Computing and Control Applications (CCCA)

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In this paper a filter based on fuzzy logic is proposed to reduce impulse noise from 2D electrical resistivity imaging data. Common types of noise in electrical resistivity data are coherent noise, due to near surface inhomogeneities or electrode effects, random or impulsive noise, resulting from telluric currents, electrode polarization or measurements with low signalto-noise ratio. The noise detection is based on computing difference between a central sample and its neighbourhood. The detected noisy sample is then removed by a fuzzy based filtering process. Experimental results show the effectiveness of our filter in reducing both coherent and impulsive noise compared to popular median filter. The comparison of the results is based on the Mean-square-error in the inversion process.

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An efficient method for the removal of impulse noise from speech and audio signals

Cited by 33 publications

References 2 publications

Impulse-noise suppression in speech using the stationary wavelet transform

Impulse-noise suppression in speech using the stationary wavelet transform

An online noise filter for eye-tracker data recorded in a virtual environment

Impulse noise reduction in 2D electrical resistivity imaging data based on fuzzy logic

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