Source coding of audio signals with a generative model
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 0; 2024: 1; 2025: 1.
7 papers.
- 2022Universal Speech Enhancement with Score-based Diffusioncited by 75
- 2022End-to-End Neural Speech Coding for Real-Time Communicationscited by 7
- 2022Latent-Domain Predictive Neural Speech Codingcited by 7
- 2022Cross-Scale Vector Quantization for Scalable Neural Speech Codingcited by 3
- 2024Audio Decoding by Inverse Problem Solvingcited by 1
- 2025Enhanced Generative Machine Listenercited by 1
- 2021Low Bit-Rate Wideband Speech Coding: A Deep Generative Model based Approachcited by 0
- 2025Enhanced Generative Machine Listener25 Sep 2025
- 2024Audio Decoding by Inverse Problem Solving12 Sep 2024
- 2022Latent-Domain Predictive Neural Speech Coding18 Jul 2022
- 2022Cross-Scale Vector Quantization for Scalable Neural Speech Coding7 Jul 2022
- 2022Universal Speech Enhancement with Score-based Diffusion7 Jun 2022
- 2022End-to-End Neural Speech Coding for Real-Time Communications24 Jan 2022
- 2021Low Bit-Rate Wideband Speech Coding: A Deep Generative Model based Approach4 Feb 2021