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dc.contributor.authorSo, Stephenen_US
dc.contributor.authorPaliwal, Kuldipen_US
dc.description.abstractIn this paper, we investigate the ML-switched split vector quantiser (ML-SSVQ), which uses the concept of multiple survivor paths to improve the rate-distortion (R-D) performance of conventional SSVQ at the cost of increasing the computational complexity. Despite the SSVQ being a state-of-the-art vector quantiser for coding line spectral frequencies, it suffers from high memory requirements. This can be alleviated by splitting the vectors into more parts though this comes at the cost of degrading R-D efficiency. We will show via wideband LSF experiments that the ML-SSVQ incurs less spectral distortion and outlier frames than conventional SSVQ at the same bitrate. These improvements have allowed the six-part ML-SSVQ to be competitive when compared with five-part SSVQ and the 46 bits/frame split-multistage vector quantiser with moving average predictor from the ITU-T G722.2 AMR-WB speech codec.en_US
dc.publisherNo data provideden_US
dc.relation.ispartofconferencenameGriffith School of Engineering Research Conferenceen_US
dc.relation.ispartofconferencetitleProceedings of the Griffith School of Engineering Research Conferenceen_US
dc.titleEfficient vector quantisation of wideband LPC parameters using the ML-SSVQen_US
dc.typeConference outputen_US
dc.type.descriptionE2 - Conference Publications (Non HERDC Eligible)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.hasfulltextNo Full Text

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

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