Analysis and removing noise from speech using wavelet transform

Karel Tomala; Miroslav Voznak; Pavol Partila; Filip Rezac; Jakub Safarik

doi:10.1117/12.2015722

29 May 2013 Analysis and removing noise from speech using wavelet transform

Karel Tomala, Miroslav Voznak, Pavol Partila, Filip Rezac, Jakub Safarik

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Proceedings Volume 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI; 87500D (2013) https://doi.org/10.1117/12.2015722
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

The paper discusses the use of Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) wavelet in removing noise from voice samples and evaluation of its impact on speech quality. One significant part of Quality of Service (QoS) in communication technology is the speech quality assessment. However, this part is seriously overlooked as telecommunication providers often focus on increasing network capacity, expansion of services offered and their enforcement in the market. Among the fundamental factors affecting the transmission properties of the communication chain is noise, either at the transmitter or the receiver side. A wavelet transform (WT) is a modern tool for signal processing. One of the most significant areas in which wavelet transforms are used is applications designed to suppress noise in signals. To remove noise from the voice sample in our experiment, we used the reference segment of the voice which was distorted by Gaussian white noise. An evaluation of the impact on speech quality was carried out by an intrusive objective algorithm Perceptual Evaluation of Speech Quality (PESQ). DWT and SWT transformation was applied to voice samples that were devalued by Gaussian white noise. Afterwards, we determined the effectiveness of DWT and SWT by means of objective algorithm PESQ. The decisive criterion for determining the quality of a voice sample once the noise had been removed was Mean Opinion Score (MOS) which we obtained in PESQ. The contribution of this work lies in the evaluation of efficiency of wavelet transformation to suppress noise in voice samples.

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Karel Tomala, Miroslav Voznak, Pavol Partila, Filip Rezac, and Jakub Safarik "Analysis and removing noise from speech using wavelet transform", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500D (29 May 2013); https://doi.org/10.1117/12.2015722

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KEYWORDS

Wavelets

Discrete wavelet transforms

Stationary wavelet transform

Wavelet transforms

Molybdenum

Denoising

Linear filtering

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