Detection and suppression of impulsive noise in speech communication systems

Vaseghi, Saeed; Rayner, P.J.W.

doi:10.1049/ip-i-2.1990.0007

Cited by 65 publications

(40 citation statements)

References 2 publications

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“…As can be seen, the noise produced by such a system occurs in bursts, where its value is precisely zero for at least some of the time. A typical audio signal degraded with impulse noise can have an average impulse width of around 1 ms while the fraction of the signal that is contaminated is usually less than 20 percent [15]. If α is the fraction of signal samples contaminated by impulse noise the average signal to impulse noise ratio is given by [16] …”

Section: Resultsmentioning

confidence: 99%

Design of optimal wavelets for detecting impulse noise in speech

Nongpiur

Shpak

Agathoklis

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Removal of impulse noise from speech in the wavelet domain has been found to be very effective due to the multi-resolution property of the wavelet transform and the ease of removing the impulses in that domain. A critical factor that affects the performance of the impulse-removal system is the effectiveness of the impulse detection algorithm. To this end, we propose a new method for designing orthogonal wavelets that are optimized for detecting impulse noise in speech. In the method, the characteristics of the impulse noise and the underlying speech signal are taken into account and a convex optimization problem is formulated for deriving the optimal wavelet for a given support size. Performance comparison with other wellknown wavelets show that the wavelets designed using the proposed method have much better impulse detection properties.Index Terms-impulsive noise detection, wavelet design, speech enhancement

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Section: Resultsmentioning

confidence: 99%

Design of optimal wavelets for detecting impulse noise in speech

Nongpiur

Shpak

Agathoklis

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Given the signal trend on the left and on the right of the missing sequence boundaries our task is to fill the gap. Using time domain based approaches known in literature it is not possible to solve the problem if the gap is more then one hundred of samples [2], [3], [5]. Better results can be obtained using frequency domain based approaches [6].…”

Section: Subband Signal Recoveringmentioning

confidence: 99%

“…We have a sequence with inside a gap of missing data that must be filled. Two different approaches are used: the first is based on a linear prediction algorithm [2][3], while the second is based on a non-linear prediction algorithm, realized using neural networks [5]. In this case a new neuron model is used characterized by an adaptive spline activation function [9].…”

Section: Subband Signal Predictionmentioning

confidence: 99%

“…A different methodology for missing data reconstruction is based on the assumption that signal can be modelled as P order autoregressive process [2][3]. The model parameters, which are the taps of a FIR linear predictor filter, are calculated using a signal block including missing data.…”

Section: Introductionmentioning

confidence: 99%

“…This method is not very appropriate for reconstructing long sequence (over about a hundred of samples at 44.1KHz sampling rate [4]). An proposed improvement involves the addition of long term prediction to short term prediction [2]. The pitch period is calculated and prediction is carried out using P samples in proximity of missing data and Q (Q<P) a pitch period away.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Subbands audio signal recovering using neural nonlinear prediction

Cocchi¹,

Uncini²

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)

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Audio signal recovery is a common problem in digital audio restoration field, because of corrupted samples that must be replaced. In this paper a subbands architecture is presented for audio signal recovery, using neural nonlinear prediction based on adaptive spline neural networks. The experimental results show the mean square reconstruction error, and maximum error obtained with increasing gap length, from 200 to 5000 samples. The method gives good results allowing the reconstruction of over 100ms signal with low audible effects in overall quality.

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Audio Restoration

Esquef¹

2008

Handbook of Signal Processing in Acoustics

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Detection and suppression of impulsive noise in speech communication systems

Cited by 65 publications

References 2 publications

Design of optimal wavelets for detecting impulse noise in speech

Design of optimal wavelets for detecting impulse noise in speech

Subbands audio signal recovering using neural nonlinear prediction

Audio Restoration

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