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Memory-based vector quantization of LSF parameters by a power series approximation

Thomas Eriksson (Institutionen för signaler och system, Kommunikationssystem) ; Fredrik Nordén (Institutionen för signaler och system, Informationsteori)
IEEE Transactions on Audio, Speech and Language Processing Vol. 15 (2007), 4,
[Artikel, refereegranskad vetenskaplig]

Abstract: In this paper, memory-based quantization is studied in detail. We propose a new framework, Power Series Quantization (PSQ), for memory-based quantization. With LSF quantization as the application, several common memory-based quantization methods (FSVQ, predictive VQ, VPQ, safety-net etc.) are analyzed and compared with the proposed method, and it is shown that the proposed method performs better than all other tested methods. The proposed PSQ method is fully general, in that it can simulate all other memory-based quantizers if it is allowed unlimited complexity.

Denna post skapades 2007-02-05. Senast ändrad 2016-02-01.
CPL Pubid: 25845


Institutioner (Chalmers)

Institutionen för signaler och system, Kommunikationssystem (1900-2017)
Institutionen för signaler och system, Informationsteori (2005-2014)



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