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Gmm-based entropy-c...
Gmm-based entropy-constrained vector quantization
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- Zhao, David Y. (författare)
- KTH,Ljud- och bildbehandling
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Samuelsson, Jonas (författare)
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- Nilsson, Mattias (författare)
- KTH,Ljud- och bildbehandling
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(creator_code:org_t)
- 2007
- 2007
- Engelska.
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Ingår i: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol IV, Pts 1-3. ; , s. 1097-1100
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
Nyckelord
- entropy constrained vector quantizer (ECVQ)
- lattice
- Gaussian mixture model (GMM)
- arithmetic coding
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)