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Joseph Steinmeyer and Ricardo Carreras. Chacon-Castaño, Julian(Julian A.) Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2021-02-19T20:39:51Z 2021-02-19T20:39:51Z 2020 2020 https://hdl.handle.net/1721.1/129887 1237279987 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020 Cataloged from student-submitted PDF of thesis. Includes bibliographical references (pages 107-108). Multi-Channel Acoustic Echo cancellation (MCAEC) is a vital component of delivering clean speech to a virtual personal assistant through a smart speaker with multi-channel audio (stereophonic, etc). The use of the Kalman filter as an alternative adaptive filter methodology for this MCAEC application is explored in this work. The Normalized Least Mean Squares filter (NLMS) serves as a benchmark for the Kalman filter. Simulations using room recordings and measured room responses are employed in this exploration. Useful metrics such as the Word Error Rate (WER) and Echo Return Loss Enhancement (ERLE) help to distinguish performance among the two adaptive filter algorithms. For the single channel case, simulations confirm the cancellation and convergence rate advantage of the Kalman filter, in full-band, but the NLMS filter gives similar results in the sub-band domain, as measured by WER and ERLE. In the multi-channel case, both solutions achieve similar steady state cancellation, but the NLMS offers slightly faster convergence rates. In experiments where adaptation was not frozen, the Kalman filter effectively maintains high echo cancellation by tracking input signal statistics. In most cases, the Kalman filter does not present an appropriate alternative for the MCAEC application in this work. by Julian Chacon-Castaño. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 108 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Exploration of alternative algorithms for multi-channel acoustic echo cancellation Thesis application/pdf 2021-02-19T20:39:21Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="008fc931-6c6b-4a30-a3e0-6d02d9cdd438"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Exploration of alternative algorithms for multi-channel acoustic echo cancellation</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Chacon-Castaño, Julian(Julian A.)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Multi-Channel Acoustic Echo cancellation (MCAEC) is a vital component of delivering clean speech to a virtual personal assistant through a smart speaker with multi-channel audio (stereophonic, etc). The use of the Kalman filter as an alternative adaptive filter methodology for this MCAEC application is explored in this work. The Normalized Least Mean Squares filter (NLMS) serves as a benchmark for the Kalman filter. Simulations using room recordings and measured room responses are employed in this exploration. Useful metrics such as the Word Error Rate (WER) and Echo Return Loss Enhancement (ERLE) help to distinguish performance among the two adaptive filter algorithms. For the single channel case, simulations confirm the cancellation and convergence rate advantage of the Kalman filter, in full-band, but the NLMS filter gives similar results in the sub-band domain, as measured by WER and ERLE. In the multi-channel case, both solutions achieve similar steady state cancellation, but the NLMS offers slightly faster convergence rates. In experiments where adaptation was not frozen, the Kalman filter effectively maintains high echo cancellation by tracking input signal statistics. In most cases, the Kalman filter does not present an appropriate alternative for the MCAEC application in this work.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>