Convolutional Bias Removal Based on Normalizing the Filterbank Spectral Magnitude

dc.contributor.author Tüfekçi, Zekeriya
dc.coverage.doi 10.1109/LSP.2006.891313
dc.date.accessioned 2016-08-15T07:22:53Z
dc.date.available 2016-08-15T07:22:53Z
dc.date.issued 2007
dc.description.abstract In this letter, a novel convolutional bias removal technique is proposed. The proposed method is based on scaling the filterbank magnitude by the average of filterbank magnitude over time. The relation between the cepstral mean normalization (CMN) and proposed algorithm is derived. The experimental results show that the proposed algorithm is more robust than the CMN for both convolutional bias and additive noise. For example, the proposed method reduced the equal error rate by 5.66% and 10.16% on average for the convolutional bias and 12-dB additive noise, respectively. en_US
dc.identifier.citation Tüfekçi, Z. (2007). Convolutional bias removal based on normalizing the filterbank spectral magnitude. IEEE Signal Processing Letters, 14(7), 485-488. doi:10.1109/LSP.2006.891313 en_US
dc.identifier.doi 10.1109/LSP.2006.891313 en_US
dc.identifier.doi 10.1109/LSP.2006.891313
dc.identifier.issn 1070-9908
dc.identifier.scopus 2-s2.0-34347393311
dc.identifier.uri http://doi.org/10.1109/LSP.2006.891313
dc.identifier.uri https://hdl.handle.net/11147/2101
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof IEEE Signal Processing Letters en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Additive noise en_US
dc.subject Convolutional noise en_US
dc.subject Robust speaker verification en_US
dc.subject Convolution en_US
dc.subject Filter banks en_US
dc.title Convolutional Bias Removal Based on Normalizing the Filterbank Spectral Magnitude en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Tüfekçi, Zekeriya
gdc.author.yokid 132910
gdc.bip.impulseclass C5
gdc.bip.influenceclass C5
gdc.bip.popularityclass C5
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department İzmir Institute of Technology. Electrical and Electronics Engineering en_US
gdc.description.endpage 488 en_US
gdc.description.issue 7 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.startpage 485 en_US
gdc.description.volume 14 en_US
gdc.description.wosquality Q2
gdc.identifier.openalex W2020063564
gdc.identifier.wos WOS:000247407000014
gdc.index.type WoS
gdc.index.type Scopus
gdc.oaire.accesstype BRONZE
gdc.oaire.diamondjournal false
gdc.oaire.impulse 1.0
gdc.oaire.influence 3.0978926E-9
gdc.oaire.isgreen true
gdc.oaire.keywords Convolutional noise
gdc.oaire.keywords Robust speaker verification
gdc.oaire.keywords Filter banks
gdc.oaire.keywords Additive noise
gdc.oaire.keywords Convolution
gdc.oaire.popularity 5.0752025E-10
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 03 medical and health sciences
gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
gdc.oaire.sciencefields 02 engineering and technology
gdc.oaire.sciencefields 0305 other medical science
gdc.openalex.collaboration National
gdc.openalex.fwci 0.93163163
gdc.openalex.normalizedpercentile 0.73
gdc.opencitations.count 6
gdc.plumx.crossrefcites 6
gdc.plumx.mendeley 4
gdc.plumx.scopuscites 7
gdc.scopus.citedcount 7
gdc.wos.citedcount 6
relation.isOrgUnitOfPublication.latestForDiscovery 9af2b05f-28ac-4003-8abe-a4dfe192da5e

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