# Synthetic training set generation using text-to-audio models for environmental sound classification

arXiv: https://arxiv.org/abs/2403.17864 · HTML: https://mlsanity.com/paper/2403.17864/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2024.

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

- [MambaFoley: Foley Sound Generation using Selective State-Space Models](https://mlsanity.com/paper/2409.09162/) (2024)
- [Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification](https://mlsanity.com/paper/2504.03329/) (2025)
- [Investigating Group Relative Policy Optimization for Diffusion Transformer based Text-to-Audio Generation](https://mlsanity.com/paper/2603.01565/) (2026)
- [SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models](https://mlsanity.com/paper/2606.06907/) (2026)

## Abstract (arXiv)

In recent years, text-to-audio models have revolutionized the field of automatic audio generation. This paper investigates their application in generating synthetic datasets for training data-driven models. Specifically, this study analyzes the performance of two environmental sound classification systems trained with data generated from text-to-audio models. We considered three scenarios: a) augmenting the training dataset with data generated by text-to-audio models; b) using a mixed training dataset combining real and synthetic text-driven generated data; and c) using a training dataset composed entirely of synthetic audio. In all cases, the performance of the classification models was tested on real data. Results indicate that text-to-audio models are effective for dataset augmentation, with consistent performance when replacing a subset of the recorded dataset. However, the performance of the audio recognition models drops when relying entirely on generated audio.