# A General Framework for Learning Procedural Audio Models of Environmental Sounds

arXiv: https://arxiv.org/abs/2303.02396 · HTML: https://mlsanity.com/paper/2303.02396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 4 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Fr'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms](https://mlsanity.com/paper/1812.08466/) (2018)
- [Efficient Neural Audio Synthesis](https://mlsanity.com/paper/1802.08435/) (2018)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Symbolic Music Generation with Diffusion Models](https://mlsanity.com/paper/2103.16091/) (2021)
- [GANSynth: Adversarial Neural Audio Synthesis](https://mlsanity.com/paper/1902.08710/) (2019)
- [Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2005.00065/) (2020)
- [MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling](https://mlsanity.com/paper/2112.09312/) (2021)
- [Speech Synthesis and Control Using Differentiable DSP](https://mlsanity.com/paper/2010.15084/) (2020)
- [A Proposal for Foley Sound Synthesis Challenge](https://mlsanity.com/paper/2207.10760/) (2022)
- [Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks](https://mlsanity.com/paper/2110.09605/) (2021)
- [Reproducible Subjective Evaluation](https://mlsanity.com/paper/2203.04444/) (2022)

## Abstract (arXiv)

This paper introduces the Procedural (audio) Variational autoEncoder (ProVE) framework as a general approach to learning Procedural Audio PA models of environmental sounds with an improvement to the realism of the synthesis while maintaining provision of control over the generated sound through adjustable parameters. The framework comprises two stages: (i) Audio Class Representation, in which a latent representation space is defined by training an audio autoencoder, and (ii) Control Mapping, in which a joint function of static/temporal control variables derived from the audio and a random sample of uniform noise is learned to replace the audio encoder. We demonstrate the use of ProVE through the example of footstep sound effects on various surfaces. Our results show that ProVE models outperform both classical PA models and an adversarial-based approach in terms of sound fidelity, as measured by Fr'echet Audio Distance (FAD), Maximum Mean Discrepancy (MMD), and subjective evaluations, making them feasible tools for sound design workflows.