State encoding of Hidden Markov linear prediction models

Vikram Krishnamurthy; H. Vincent Poor

doi:10.1109/JCN.1999.6596945

State encoding of Hidden Markov linear prediction models

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we derive finite-dimensional non-linear filters for optimally reconstructing speech signals in Switched Prediction vocoders, Code Excited Linear Prediction (CELP) and Differential Pulse Code Modulation (DPCM). Our filter is an extension of the Hidden Markov filter.

Original language	English (US)
Pages (from-to)	153-157
Number of pages	5
Journal	Journal of Communications and Networks
Volume	1
Issue number	3
DOIs	https://doi.org/10.1109/JCN.1999.6596945
State	Published - Jan 1 1999

All Science Journal Classification (ASJC) codes

Information Systems
Computer Networks and Communications

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10.1109/JCN.1999.6596945

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title = "State encoding of Hidden Markov linear prediction models",

abstract = "In this paper, we derive finite-dimensional non-linear filters for optimally reconstructing speech signals in Switched Prediction vocoders, Code Excited Linear Prediction (CELP) and Differential Pulse Code Modulation (DPCM). Our filter is an extension of the Hidden Markov filter.",

author = "Vikram Krishnamurthy and Poor, {H. Vincent}",

year = "1999",

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doi = "10.1109/JCN.1999.6596945",

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journal = "Journal of Communications and Networks",

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AU - Poor, H. Vincent

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AB - In this paper, we derive finite-dimensional non-linear filters for optimally reconstructing speech signals in Switched Prediction vocoders, Code Excited Linear Prediction (CELP) and Differential Pulse Code Modulation (DPCM). Our filter is an extension of the Hidden Markov filter.

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DO - 10.1109/JCN.1999.6596945

M3 - Article

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JO - Journal of Communications and Networks

JF - Journal of Communications and Networks

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State encoding of Hidden Markov linear prediction models

Abstract

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