NEURAL AUDIO CODEC

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.