End-to-End Neural Audio Coding in the MDCT Domain

By Hyungseob Lim, Jihyun Lee, Byeong Hyeon Kim, Inseon Jang, Hong-Goo Kang. 2023

5 citations recorded by the source corpus.

Modern deep neural network (DNN)-based audio coding approaches utilize complicated non-linear functions (e.g., convolutional neural networks and non-linear activations), which leads to high complexity and memory usage. However, their decoded audio quality is still not much higher than that of signal processing-based legacy codecs. In this paper, we propose an effective frequency-domain neural audio coding paradigm that adopts the modified discrete cosine transform (MDCT) for analysis and synthesis and DNNs for the quantization of variables. It includes an efficient method to encode MDCT bins as well as a mechanism to adapt the quantization level of each bin. Our neural audio codec is trained in an end-to-end manner with the help of psychoacoustics-based perceptual loss, removing the burden of module-by-module fine-tuning. Experimental results show that our proposed model’s performance is comparable with the MP3 codec at around 64 and 48 kbps bit-rates for mono signals.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Shiliang Zhang
2. Liang Zheng
3. Yi Yang
4. Qi Tian

In Signal Processing

1. Zhihao Du
2. Shiliang Zhang
3. Kai Hu
4. Siqi Zheng

Citations in the corpus

• FunCodec: A Fundamental, Reproducible and Integrable Open-Source Toolkit for Neural Speech Codec (2023, background): “Additionally, the modified discrete cosine transform (MDCT) domain has also been explored [14,15].”