Postfiltering Using Log-Magnitude Spectrum for Speech and Audio Coding

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Das, Sneha Bäckström, Tom 2018-12-10T10:36:16Z 2018-12-10T10:36:16Z 2018-09
dc.identifier.citation Das , S & Bäckström , T 2018 , Postfiltering Using Log-Magnitude Spectrum for Speech and Audio Coding . in Interspeech : Annual Conference of the International Speech Communication Association . , 1027 , Interspeech , International Speech Communication Association , pp. 3543-3547 , Interspeech , Hyberabad , India , 02/09/2018 . DOI: 10.21437/Interspeech.2018-1027 en
dc.identifier.issn 1990-9772
dc.identifier.other PURE UUID: fa7536e0-8772-4617-bf68-af6f77d4df64
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE FILEURL:
dc.description.abstract Advanced coding algorithms yield high quality signals with good coding efficiency within their target bit-rate ranges, but their performance suffer outside the target range. At lower bitrates, the degradation in performance is because the decoded signals are sparse, which gives a perceptually muffled and distorted characteristic to the signal. Standard codecs reduce such distortions by applying noise filling and post-filtering methods. In this paper, we propose a post-processing method based on modeling the inherent time-frequency correlation in the log-magnitude spectrum. The goal is to improve the perceptual SNR of the decoded signals and, to reduce the distortions caused by signal sparsity. Objective measures show an average improvement of 1.5 dB for input perceptual SNR in range 4 to 18 dB. The improvement is especially prominent in components which had been quantized to zero. en
dc.format.extent 3543-3547
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartof Interspeech en
dc.relation.ispartofseries Interspeech en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title Postfiltering Using Log-Magnitude Spectrum for Speech and Audio Coding en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Signal Processing and Acoustics
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201812106385
dc.identifier.doi 10.21437/Interspeech.2018-1027
dc.type.version publishedVersion

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