# Source coding of audio signals with a generative model

arXiv: https://arxiv.org/abs/2001.09847 · HTML: https://mlsanity.com/paper/2001.09847/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [Universal Speech Enhancement with Score-based Diffusion](https://mlsanity.com/paper/2206.03065/) (2022)
- [End-to-End Neural Speech Coding for Real-Time Communications](https://mlsanity.com/paper/2201.09429/) (2022)
- [Latent-Domain Predictive Neural Speech Coding](https://mlsanity.com/paper/2207.08363/) (2022)
- [Cross-Scale Vector Quantization for Scalable Neural Speech Coding](https://mlsanity.com/paper/2207.03067/) (2022)
- [Audio Decoding by Inverse Problem Solving](https://mlsanity.com/paper/2409.07858/) (2024)
- [Enhanced Generative Machine Listener](https://mlsanity.com/paper/2509.21463/) (2025)
- [Low Bit-Rate Wideband Speech Coding: A Deep Generative Model based Approach](https://mlsanity.com/paper/2102.02640/) (2021)

## Abstract (arXiv)

We consider source coding of audio signals with the help of a generative model. We use a construction where a waveform is first quantized, yielding a finite bitrate representation. The waveform is then reconstructed by random sampling from a model conditioned on the quantized waveform. The proposed coding scheme is theoretically analyzed. Using SampleRNN as the generative model, we demonstrate that the proposed coding structure provides performance competitive with state-of-the-art source coding tools for specific categories of audio signals.