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Gmm-based entropy-constrained vector quantization
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2007 (English)In: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol IV, Pts 1-3, 2007, 1097-1100 p.Conference paper (Refereed)
Abstract [en]

In this paper, we present a scalable entropy-constrained vector quantizer based on Gaussian mixture models (GMMs), lattice quantization, and arithmetic coding. We assume that the source has a probability density function of a GMM. The scheme is based on a mixture component classifier, the Karhunen Loeve transform of the component, followed by a lattice quantization. The scalar elements of the quantized vector are entropy coded using a specially designed arithmetic coder. The proposed scheme has a computational complexity that is independent of rate, and quadratic with respect to vector dimension. The design is flexible and allows for adjusting the desired target rate on-the-fly. We evaluated the performance of the proposed scheme on speech-derived source vectors. It was demonstrated that the proposed scheme outperforms a fixed-rate GMM based vector quantizer, and performs closely to the theoretical optimum.

Place, publisher, year, edition, pages
2007. 1097-1100 p.
, International Conference on Acoustics Speech and Signal Processing (ICASSP), ISSN 1520-6149
Keyword [en]
entropy constrained vector quantizer (ECVQ), lattice, Gaussian mixture model (GMM), arithmetic coding
National Category
URN: urn:nbn:se:kth:diva-40753ISI: 000248909200275ScopusID: 2-s2.0-34547551858OAI: diva2:443127
32nd IEEE International Conference on Acoustics, Speech and Signal Processing Location: Honolulu, HI Date: APR 15-20, 2007
Available from: 2011-09-23 Created: 2011-09-20 Last updated: 2011-09-23Bibliographically approved

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