End-to-End Optimization of Source Models for Speech and Audio Coding Using a Machine Learning Framework

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Journal Title
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Volume Title
Conference article in proceedings
Date
2019-09
Major/Subject
Mcode
Degree programme
Language
en
Pages
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Proceedings of Interspeech, Interspeech - Annual Conference of the International Speech Communication Association
Abstract
Speech coding is the most commonly used application of speech processing. Accumulated layers of improvements have however made codecs so complex that optimization of individual modules becomes increasingly difficult. This work introduces machine learning methodology to speech and audio coding, such that we can optimize quality in terms of overall entropy. We can then use conventional quantization, coding and perceptual models without modification such that the codec adheres to conventional requirements on algorithmic complexity, latency and robustness to packet loss. Experiments demonstrate that end-to-end optimization of quantization accuracy of the spectral envelope can be used for a lossless reduction in bitrate of 0.4 kbits/s.
Description
Keywords
speech and audio coding, end-to-end optimization, speech source modeling
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Citation
Bäckström , T 2019 , End-to-End Optimization of Source Models for Speech and Audio Coding Using a Machine Learning Framework . in Proceedings of Interspeech . Interspeech - Annual Conference of the International Speech Communication Association , International Speech Communication Association (ISCA) , pp. 3401-3405 , Interspeech , Graz , Austria , 15/09/2019 . https://doi.org/10.21437/Interspeech.2019-1284