AudioDec: An Open-source Streaming High-fidelity Neural Audio Codec
Abstract
A good audio codec for live applications such as telecommunication is characterized by three key properties: (1) compression, i.e.\ the bitrate that is required to transmit the signal should be as low as possible; (2) latency, i.e.\ encoding and decoding the signal needs to be fast enough to enable communication without or with only minimal noticeable delay; and (3) reconstruction quality of the signal. In this work, we propose an open-source, streamable, and real-time neural audio codec that achieves strong performance along all three axes: it can reconstruct highly natural sounding 48~kHz speech signals while operating at only 12~kbps and running with less than 6~ms (GPU)/10~ms (CPU) latency. An efficient training paradigm is also demonstrated for developing such neural audio codecs for real-world scenarios. Both objective and subjective evaluations using the VCTK corpus are provided. To sum up, AudioDec is a well-developed plug-and-play benchmark for audio codec applications.
Keywords
Cite
@article{arxiv.2305.16608,
title = {AudioDec: An Open-source Streaming High-fidelity Neural Audio Codec},
author = {Yi-Chiao Wu and Israel D. Gebru and Dejan Marković and Alexander Richard},
journal= {arXiv preprint arXiv:2305.16608},
year = {2023}
}
Comments
5 pages, 1 figure, 5 tables. Proc. ICASSP, 2023