NEURAL AUDIO CODEC
Date
2023-04-30
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E. Pulchowk Campus
Abstract
Neural audio codecs that use end-to-end approaches have gained popularity due to their
ability to learn efficient audio representations through data-driven methods, without relying
on handcrafted signal processing components. This research paper evaluates the performance
of Neural Audio Codec in comparison to traditional audio codecs Opus and EVS in
terms of audio quality and efficiency. The study highlights the limitations of existing audio
codecs in leveraging the abundant data available in the audio compression pipeline and
proposes deep learning-based models as a potential solution. The paper reviews recent advancements
in deep learning-based audio synthesis and representation learning and explores
the potential of deep learning-based audio codecs in enhancing compression efficiency. The
study also addresses the limitations of existing models, including slower training times and
increased memory requirements, by releasing open-source code and pre-trained models for
further research and improvement. Experimental results show that our approach has comparable
performance to widely used commercial codec OPUS at low bitrate, and a slight drop
in performance compared to current deep learning-based frameworks but at the expense of
significant improvement in speed and memory requirements. We have released our code and
pre-trained models at https://github.com/AchyutBurlakoti/Neural-Audio-Compression for
further research and improvement.
Description
Neural audio codecs that use end-to-end approaches have gained popularity due to their
ability to learn efficient audio representations through data-driven methods, without relying
on handcrafted signal processing components.
Keywords
Audio Compression,, Deep Learning,, Audio Codec