# MDCTCodec: A Lightweight MDCT-based Neural Audio Codec towards High Sampling Rate and Low Bitrate Scenarios

arXiv: https://arxiv.org/abs/2411.00464 · HTML: https://mlsanity.com/paper/2411.00464/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2024.

## Built on by (most cited first, 4 of 4)

- [The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs](https://mlsanity.com/paper/2602.15491/) (2026)
- [A High-Quality and Low-Complexity Streamable Neural Speech Codec with Knowledge Distillation](https://mlsanity.com/paper/2509.13670/) (2025)
- [Decoder-Side Semantic Conditioning for Low-Bitrate Neural Speech Compression](https://mlsanity.com/paper/2512.21653/) (2025)
- [TokenMapper: A Step Toward Interoperable Speech Token Translation](https://mlsanity.com/paper/2609.12563/) (2026)

## Abstract (arXiv)

In this paper, we propose MDCTCodec, an efficient lightweight end-to-end neural audio codec based on the modified discrete cosine transform (MDCT). The encoder takes the MDCT spectrum of audio as input, encoding it into a continuous latent code which is then discretized by a residual vector quantizer (RVQ). Subsequently, the decoder decodes the MDCT spectrum from the quantized latent code and reconstructs audio via inverse MDCT. During the training phase, a novel multi-resolution MDCT-based discriminator (MR-MDCTD) is adopted to discriminate the natural or decoded MDCT spectrum for adversarial training. Experimental results confirm that, in scenarios with high sampling rates and low bitrates, the MDCTCodec exhibited high decoded audio quality, improved training and generation efficiency, and compact model size compared to baseline codecs. Specifically, the MDCTCodec achieved a ViSQOL score of 4.18 at a sampling rate of 48 kHz and a bitrate of 6 kbps on the public VCTK corpus.