# Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2110.09605 · HTML: https://mlsanity.com/paper/2110.09605/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks](https://mlsanity.com/paper/2008.12073/) (2020)
- [VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network](https://mlsanity.com/paper/2007.15256/) (2020)
- [GAN Vocoder: Multi-Resolution Discriminator Is All You Need](https://mlsanity.com/paper/2103.05236/) (2021)
- [One Billion Audio Sounds from GPU-enabled Modular Synthesis](https://mlsanity.com/paper/2104.12922/) (2021)
- [Neural Drum Machine : An Interactive System for Real-time Synthesis of Drum Sounds](https://mlsanity.com/paper/1907.02637/) (2019)
- [Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform Generation in Multiple Domains](https://mlsanity.com/paper/2011.09631/) (2020)
- [Neural Percussive Synthesis Parameterised by High-Level Timbral Features](https://mlsanity.com/paper/1911.11853/) (2019)
- [Conditional WaveGAN](https://mlsanity.com/paper/1809.10636/) (2018)
- [Improved parallel WaveGAN vocoder with perceptually weighted spectrogram loss](https://mlsanity.com/paper/2101.07412/) (2021)

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

- [Full-band General Audio Synthesis with Score-based Diffusion](https://mlsanity.com/paper/2210.14661/) (2022)
- [T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesis](https://mlsanity.com/paper/2401.09294/) (2024)
- [FolAI: Synchronized Foley Sound Generation with Semantic and Temporal Alignment](https://mlsanity.com/paper/2412.15023/) (2024)
- [A General Framework for Learning Procedural Audio Models of Environmental Sounds](https://mlsanity.com/paper/2303.02396/) (2023)

## Abstract (arXiv)

Footsteps are among the most ubiquitous sound effects in multimedia applications. There is substantial research into understanding the acoustic features and developing synthesis models for footstep sound effects. In this paper, we present a first attempt at adopting neural synthesis for this task. We implemented two GAN-based architectures and compared the results with real recordings as well as six traditional sound synthesis methods. Our architectures reached realism scores as high as recorded samples, showing encouraging results for the task at hand.