End-To-End Optimized Speech Coding with Deep Neural Networks

Srihari Kankanahalli

ICASSP 2018accepted0 citations

End-To-End Optimized Speech Coding with Deep Neural Networks

Abstract

Modern compression algorithms are often the result of laborious domain-specific research; industry standards such as MP3, JPEG, and AMR-WB took years to develop and were largely hand-designed. We present a deep neural network model which optimizes all the steps of a wideband speech coding pipeline (compression, quantization, entropy coding, and decompression) end-to-end directly from raw speech data - no manual feature engineering necessary, and it trains in hours. In testing, our DNN-based coder performs on par with the AMR -WB standard at a variety of bitrates (~9kbps up to ~24kbps). It also runs in realtime on a 3.8GhZ Intel CPU.

BibTeX

@inproceedings{icassp2018_endtoendoptimize,
  title = {End-To-End Optimized Speech Coding with Deep Neural Networks},
  author = {Srihari Kankanahalli},
  booktitle = {ICASSP 2018},
  year = {2018}
}