Encoding spectral parameters using cache codebook

Simple item page
dc.contributor.authorGuerchi, Driss
dc.contributor.authorRekik, Siwar
dc.date.accessioned2020-10-01T12:59:13Z
dc.date.available2020-10-01T12:59:13Z
dc.date.copyright2012
dc.date.issued2012
dc.descriptionThis conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in Communications in Computer and Information Science (2012), available online at: https://doi.org/10.1007/978-3-642-30507-8_24en_US
dc.description.abstractA new efficient approach to quantize the spectral line frequencies (LSF) in a coder is proposed. The use of the full search algorithm in the spectral parameters quantization causes high complexity and large hardware storage. Attempts to reduce the complexity have been performed by lowering the size of the LSF codebook. This option leads to a sub-optimal solution; the number of LSF vectors to be tested affects the performance of the speech coder. Cache codebook (CCB) technique enhances the search of the optimal quantized spectral information. In this technique the size of the main codebook is kept unchanged while the number of closest match searches is reduced. Unlike the classical quantizer design, the CCB method involves one main codebook embedding four disjoint sub-codebooks. The content of the CCB at any time is an exact reproduction of one of the four sub-codebooks. The search for the best match to an input vector is limited to the LSF vectors of the CCB. Some criteria are used to accept or reject this closest match. The CCB is updated whenever the decision is in favor of rejection. The cache codebook was successfully embedded in a CELP coder to enhance the quantization of the spectral information. The comparison simulation results show that the Codebook Caching approach yields to comparable objective and subjective performance to that of the optimal full-search technique when using the same training and testing database. © Springer-Verlag Berlin Heidelberg 2012.en_US
dc.description.sponsorshipBCS The Chartered Institute for IT,Springer,IEEE - UAE Computer Sectionen_US
dc.identifier.citationGuerchi, D., Rekik, S. (2012). Encoding Spectral Parameters Using Cache Codebook. In: R. Benlamri (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30507-8_24en_US
dc.identifier.isbn978-364230506-1
dc.identifier.issn18650929
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-642-30507-8_24
dc.identifier.urihttp://hdl.handle.net/20.500.12519/244
dc.language.isoenen_US
dc.publisherSpringer Nature Switzerland AGen_US
dc.relationAuthors Affiliations : Guerchi, D., Canadian University of Dubai, Sheikh Zayed Road, Behind Shangrila Hotel, P.O.BOX 117781, Dubai, United Arab Emirates; Rekik, S., Canadian University of Dubai, Sheikh Zayed Road, Behind Shangrila Hotel, P.O.BOX 117781, Dubai, United Arab Emirates
dc.relation.ispartofseriesCommunications in Computer and Information Science;293 PART 1
dc.relation.urihttps://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=fc196109-d329-4158-94aa-0361ddca8e61
dc.rightsLicense to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © Springer-Verlag Berlin Heidelberg 2012
dc.rights.licenseLicense Number: 5241281331836 License date: Feb 03, 2022
dc.subjectCache codebooken_US
dc.subjectCELP codingen_US
dc.subjectFull search algorithmen_US
dc.subjectLpc analysisen_US
dc.subjectSpectral informationen_US
dc.subjectSpectral line frequenciesen_US
dc.subjectSubjective performanceen_US
dc.subjectTraining and testingen_US
dc.subjectComputer scienceen_US
dc.subjectOptimizationen_US
dc.titleEncoding spectral parameters using cache codebooken_US
dc.typeConference Paperen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Access Instruction 244.pdf
Size:
45.09 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Department of Computer Engineering and Computational Sciences