High Quality Audio Coding with Mdctnet

By G. Davidson, M. Vinton, P. Ekstrand, Cong Zhou, Lars F. Villemoes, Lie Lu. 2022

11 citations recorded by the source corpus.

We propose a neural audio generative model, MDCTNet, operating in the perceptually weighted domain of an adaptive modified discrete cosine transform (MDCT). The architecture of the model captures correlations in both time and frequency directions with recurrent layers (RNNs). An audio coding system is obtained by training MDCTNet on a diverse set of fullband monophonic audio signals at 48 kHz sampling, conditioned by a perceptual audio encoder. In a subjective listening test with ten excerpts chosen to be balanced across content types, yet critical for both codecs, the mean performance of the proposed system for 24 kb/s variable bitrate (VBR) is similar to that of Opus at twice the bitrate.

Subfields: Computational Mechanics, 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 Computational Mechanics

1. Shiliang Zhang

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].”