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dc.contributor.authorRoy, SK
dc.contributor.authorPaliwal, KK
dc.date.accessioned2021-05-27T04:17:10Z
dc.date.available2021-05-27T04:17:10Z
dc.date.issued2020
dc.identifier.isbn9781665419741
dc.identifier.doi10.1109/CSDE50874.2020.9411565
dc.identifier.urihttp://hdl.handle.net/10072/404714
dc.description.abstractSpeech enhancement using Kalman filter (KF) suffers from inaccurate estimates of the noise variance and the linear prediction coefficients (LPCs) in real-life noise conditions. This causes a degraded speech enhancement performance. In this paper, a causal convolutional neural network (CCNN) model is used to more accurately estimate the noise variance and LPC parameters of the KF for speech enhancement in real-life noise conditions. Specifically, a CCNN model gives an instantaneous estimate of the noise waveform for each noisy speech frame to compute the noise variance. Each noisy speech frame is pre-whitened by a whitening filter, which is constructed with the coefficients computed from the estimated noise. The LPC parameters are computed from the pre-whitened speech. The improved noise variance and LPCs enables the KF to minimize residual noise as well as distortion in the enhanced speech. Objective and subjective testing on NOIZEUS corpus reveal that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods in various noise conditions for a wide range of SNR levels.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofconferencenameIEEE Asia Pacific Conference on Computer Science and Data Engineering (CSDE)
dc.relation.ispartofconferencetitle2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
dc.relation.ispartofdatefrom2020-12-16
dc.relation.ispartofdateto2020-12-18
dc.relation.ispartoflocationGold Coast, Australia
dc.subject.fieldofresearchElectrical engineering
dc.subject.fieldofresearchElectronics, sensors and digital hardware
dc.subject.fieldofresearchNanotechnology
dc.subject.fieldofresearchcode4008
dc.subject.fieldofresearchcode4009
dc.subject.fieldofresearchcode4018
dc.titleCausal Convolutional Neural Network-Based Kalman Filter for Speech Enhancement
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationRoy, SK; Paliwal, KK, Causal Convolutional Neural Network-Based Kalman Filter for Speech Enhancement, 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020, 2020
dc.date.updated2021-05-26T22:51:03Z
gro.hasfulltextNo Full Text
gro.griffith.authorPaliwal, Kuldip K.
gro.griffith.authorRoy, Sujan Kumar K.


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    Contains papers delivered by Griffith authors at national and international conferences.

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