KLT-based adaptive classified VQ of the speech signal
2004 (English)In: IEEE transactions on speech and audio processing, ISSN 1063-6676, E-ISSN 1558-2353, Vol. 12, no 3, 277-289 p.Article in journal (Refereed) Published
Compared to scalar quantization (SQ), vector quantization (VQ) has memory space-filling, and shape advantages. If the signal statistics are known, direct vector quantization (DVQ) according to these statistics provides the highest coding efficiency, but requires unmanageable storage requirements if the statistics are time varying. In code-excited linear predictive (CELP) coding, a single compromise codebook is trained in the excitation-domain and the space-filling and shape advantages of VQ are utilized in a nonoptimal, average sense. In this paper, we propose Karhunen-Loeve transform (KLT)-based adaptive classified VQ (CVQ), where the space-filling advantage can be utilized since the Voronoi-region shape is not affected by the KLT. The memory and shape advantages can be also used, since each codebook is designed based on a narrow class of KLT-domain statistics. We further improve basic KLT-CVQ with companding. The companding utilizes the shape advantage of VQ more efficiently. Our experiments show that KLT-CVQ provides a higher SNR than basic CELP coding, and has a computational complexity similar to DVQ and much lower than CELP. With companding, even singie-class KLT-CVQ outperforms CELP, both in terms of SNR and codebook search complexity.
Place, publisher, year, edition, pages
2004. Vol. 12, no 3, 277-289 p.
code-excited linear predictive (CELP) coding, companding, Karhunen-Loeve transform (KIT), scalar quantization (SQ), speech coding, vector quantization (VQ), vector quantization
IdentifiersURN: urn:nbn:se:kth:diva-23357DOI: 10.1109/tsa.2004.825661ISI: 000221002300007ScopusID: 2-s2.0-2442551861OAI: oai:DiVA.org:kth-23357DiVA: diva2:342055
QC 201005252010-08-102010-08-102011-11-01Bibliographically approved