# Foley Sound Synthesis at the DCASE 2023 Challenge

arXiv: https://arxiv.org/abs/2304.12521 · HTML: https://mlsanity.com/paper/2304.12521/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2023.

## Built on by (most cited first, 19 of 19)

- [LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models](https://mlsanity.com/paper/2410.09732/) (2024)
- [WavJourney: Compositional Audio Creation with Large Language Models](https://mlsanity.com/paper/2307.14335/) (2023)
- [KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation](https://mlsanity.com/paper/2502.15602/) (2025)
- [UniForm: A Unified Multi-Task Diffusion Transformer for Audio-Video Generation](https://mlsanity.com/paper/2502.03897/) (2025)
- [T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesis](https://mlsanity.com/paper/2401.09294/) (2024)
- [EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis](https://mlsanity.com/paper/2311.08667/) (2023)
- [EnvSDD: Benchmarking Environmental Sound Deepfake Detection](https://mlsanity.com/paper/2505.19203/) (2025)
- [Text-Driven Foley Sound Generation With Latent Diffusion Model](https://mlsanity.com/paper/2306.10359/) (2023)
- [Generation or Replication: Auscultating Audio Latent Diffusion Models](https://mlsanity.com/paper/2310.10604/) (2023)
- [SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation](https://mlsanity.com/paper/2405.18503/) (2024)
- [Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation](https://mlsanity.com/paper/2410.17589/) (2024)
- [Latent Diffusion Model Based Foley Sound Generation System For DCASE Challenge 2023 Task 7](https://mlsanity.com/paper/2305.15905/) (2023)
- [A Demand-Driven Perspective on Generative Audio AI](https://mlsanity.com/paper/2307.04292/) (2023)
- [MambaFoley: Foley Sound Generation using Selective State-Space Models](https://mlsanity.com/paper/2409.09162/) (2024)
- [VIFS: An End-to-End Variational Inference for Foley Sound Synthesis](https://mlsanity.com/paper/2306.05004/) (2023)
- [FALL-E: A Foley Sound Synthesis Model and Strategies](https://mlsanity.com/paper/2306.09807/) (2023)
- [Latent CLAP Loss for Better Foley Sound Synthesis](https://mlsanity.com/paper/2403.12182/) (2024)
- [Audio Palette: A Diffusion Transformer with Multi-Signal Conditioning for Controllable Foley Synthesis](https://mlsanity.com/paper/2510.12175/) (2025)
- [Exploring Efficient Waveform Diffusion Models for Foley Sound Generation](https://mlsanity.com/paper/2607.29148/) (2026)

## Abstract (arXiv)

The addition of Foley sound effects during post-production is a common technique used to enhance the perceived acoustic properties of multimedia content. Traditionally, Foley sound has been produced by human Foley artists, which involves manual recording and mixing of sound. However, recent advances in sound synthesis and generative models have generated interest in machine-assisted or automatic Foley synthesis techniques. To promote further research in this area, we have organized a challenge in DCASE 2023: Task 7 - Foley Sound Synthesis. Our challenge aims to provide a standardized evaluation framework that is both rigorous and efficient, allowing for the evaluation of different Foley synthesis systems. We received 17 submissions, and performed both objective and subjective evaluation to rank them according to three criteria: audio quality, fit-to-category, and diversity. Through this challenge, we hope to encourage active participation from the research community and advance the state-of-the-art in automatic Foley synthesis. In this technical report, we provide a detailed overview of the Foley sound synthesis challenge, including task definition, dataset, baseline, evaluation scheme and criteria, challenge result, and discussion.