A wavelet-based forward BSS algorithm for acoustic noise reduction and speech enhancement
Applied Acoustics, ISSN: 0003-682X, Vol: 105, Page: 55-66
2016
- 25Citations
- 22Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
In this paper, we address the problem of noise reduction and speech enhancement by adaptive filtering algorithm. Recently, the well known forward blind source separation (FBSS) structure has been largely studied and intensively used to reduce acoustic noise components and to enhance speech signal. The FBSS structure is often combined with adaptive algorithms to accelerate the adaptation of the cross-filters, and to improve noise suppression at the output. In this paper, we propose to use a wavelet transform decomposition in the FBSS structure by using a two-channel forward wavelet symmetric adaptive decorrelating (WFSAD) algorithm. The proposed WFSAD algorithm provides a better compromise between time and frequency resolution and improves robustness of the noise reduction process when compared with the classical two-channel forward symmetric adaptive decorrelating (FSAD) algorithm. Simulation results prove the efficiency of the proposed WFBSS algorithm in comparison with conventional ones in terms of several objective and subjective criteria.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0003682X15003357; http://dx.doi.org/10.1016/j.apacoust.2015.11.011; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84949818212&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0003682X15003357; https://dx.doi.org/10.1016/j.apacoust.2015.11.011
Elsevier BV
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