# Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation

arXiv: https://arxiv.org/abs/2410.17589 · HTML: https://mlsanity.com/paper/2410.17589/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Clotho: An Audio Captioning Dataset](https://mlsanity.com/paper/1910.09387/) (2019)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://mlsanity.com/paper/2301.12661/) (2023)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://mlsanity.com/paper/2303.17395/) (2023)
- [Fr'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms](https://mlsanity.com/paper/1812.08466/) (2018)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [Effectively Unbiased FID and Inception Score and where to find them](https://mlsanity.com/paper/1911.07023/) (2019)
- [Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization](https://mlsanity.com/paper/2404.09956/) (2024)
- [Adapting Frechet Audio Distance for Generative Music Evaluation](https://mlsanity.com/paper/2311.01616/) (2023)
- [Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation](https://mlsanity.com/paper/2401.01044/) (2024)
- [ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation](https://mlsanity.com/paper/2309.10740/) (2023)
- [Foley Sound Synthesis at the DCASE 2023 Challenge](https://mlsanity.com/paper/2304.12521/) (2023)
- [Improving Text-To-Audio Models with Synthetic Captions](https://mlsanity.com/paper/2406.15487/) (2024)
- [Correlation of Fr'echet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant](https://mlsanity.com/paper/2403.17508/) (2024)
- [A Proposal for Foley Sound Synthesis Challenge](https://mlsanity.com/paper/2207.10760/) (2022)

## Built on by (most cited first, 6 of 6)

- [KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation](https://mlsanity.com/paper/2502.15602/) (2025)
- [Hear What Matters! Text-conditioned Selective Video-to-Audio Generation](https://mlsanity.com/paper/2512.02650/) (2025)
- [Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification](https://mlsanity.com/paper/2504.03329/) (2025)
- [MoEScore: Mixture-of-Experts-Based Text-Audio Relevance Score Prediction for Text-to-Audio System Evaluation](https://mlsanity.com/paper/2601.06829/) (2026)
- [Investigating Group Relative Policy Optimization for Diffusion Transformer based Text-to-Audio Generation](https://mlsanity.com/paper/2603.01565/) (2026)
- [SsgCaps: A controlled dataset for the evaluation of sound scene generation algorithms](https://mlsanity.com/paper/2609.26854/) (2026)

## Abstract (arXiv)

Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation. This paper addresses these issues through the Sound Scene Synthesis challenge held as part of the Detection and Classification of Acoustic Scenes and Events 2024. We present an evaluation protocol combining objective metric, namely Fr'echet Audio Distance, with perceptual assessments, utilizing a structured prompt format to enable diverse captions and effective evaluation. Our analysis reveals varying performance across sound categories and model architectures, with larger models generally excelling but innovative lightweight approaches also showing promise. The strong correlation between objective metrics and human ratings validates our evaluation approach. We discuss outcomes in terms of audio quality, controllability, and architectural considerations for text-to-audio synthesizers, providing direction for future research.