Weibull M-transform least mean square algorithm

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dc.contributor.author Kumar, Krishna
dc.contributor.author George, Nithin V.
dc.date.accessioned 2020-07-20T06:02:28Z
dc.date.available 2020-07-20T06:02:28Z
dc.date.issued 2020-12
dc.identifier.citation Kumar, Krishna and George, Nithin V., "Weibull M-transform least mean square algorithm", Applied Acoustics, DOI: 10.1016/j.apacoust.2020.107488, vol. 170, Dec. 2020. en_US
dc.identifier.issn 0003-682X
dc.identifier.uri http://dx.doi.org/10.1016/j.apacoust.2020.107488
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5550
dc.description.abstract This paper proposes a new robust learning strategy, which is based on a Weibull M-transform function. The suitability of the Weibull M-transform function as a robust norm has been investigated for different shape and scale parameters, and a Weibull M-transform least mean square (WMLMS) algorithm has been developed. Further, the bound of learning rate has been derived for the proposed algorithm. The proposed WMLMS algorithm has been evaluated for the problem of system identification and simulation studies carried out demonstrate its robustness. In addition, a filtered-x WMLMS (Fx-WMLMS) algorithm has been developed for robust room equalization and has been shown to offer stable room equalization even in the presence of strong disturbances picked up by the microphone.
dc.description.statementofresponsibility by Krishna Kumar and Nithin V. George
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.subject Room equalization en_US
dc.subject Adaptive filter en_US
dc.subject Correntropy criterion en_US
dc.subject Filtered-x least mean square algorithm en_US
dc.subject Acoustic path en_US
dc.title Weibull M-transform least mean square algorithm en_US
dc.type Article en_US
dc.relation.journal Applied Acoustics


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