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Second generation and perceptual wavelet based noise estimation

Date
2004
Abstract
The implementation of three noise estimation algorithms using two different signal decomposition methods: a second-generation wavelet transform and a perceptual wavelet packet transform are described in this paper. The algorithms, which do not require the use of a speech activity detector or signal statistics learning histograms, are: a smoothing-based adaptive technique, a minimum variance tracking-based technique and a quantile-based technique. The paper also proposes a new, robust noise estimation technique, which combines a quantile-based algorithm with smoothing-based algorithm. The performance of the latter technique is then evaluated and compared to those of the above three noise estimation methods under various noise conditions. Reported results demonstrate that all four algorithms are capable of tracking both stationary and non-stationary noise adequately but with varying degree of accuracy
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Description
peer-reviewed
Publisher
World Scientific and Engineering Academy and Society (WSEAS)
Citation
WSEAS Transactions on Computers, 3 (4), pp.1097-1101
Collections
Electronic & Computer Engineering
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Mahdi_2004_second.pdf
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Keywords
speech processing, wavelet-transform, second-generation wavelet transform, noise estimation
ULRR Identifiers
https://researchrepository.ul.ie/handle/10344/5112
 https://doi.org/10.34961/3590
Funding code
Funding Information
Sustainable Development Goals
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Type
Article
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/
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