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Open AccessArticle

Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106, USA
Information 2019, 10(5), 179; https://doi.org/10.3390/info10050179
Received: 14 April 2019 / Revised: 15 May 2019 / Accepted: 19 May 2019 / Published: 22 May 2019
We write the mutual information between an input speech utterance and its reconstruction by a code-excited linear prediction (CELP) codec in terms of the mutual information between the input speech and the contributions due to the short-term predictor, the adaptive codebook, and the fixed codebook. We then show that a recently introduced quantity, the log ratio of entropy powers, can be used to estimate these mutual informations in terms of bits/sample. A key result is that for many common distributions and for Gaussian autoregressive processes, the entropy powers in the ratio can be replaced by the corresponding minimum mean squared errors. We provide examples of estimating CELP codec performance using the new results and compare these to the performance of the adaptive multirate (AMR) codec and other CELP codecs. Similar to rate distortion theory, this method only needs the input source model and the appropriate distortion measure. View Full-Text
Keywords: autoregressive models; entropy power; linear prediction model; CELP voice codecs; mutual information autoregressive models; entropy power; linear prediction model; CELP voice codecs; mutual information
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Gibson, J. Mutual Information, the Linear Prediction Model, and CELP Voice Codecs. Information 2019, 10, 179.

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