# Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification

arXiv: https://arxiv.org/abs/2504.03329 · HTML: https://mlsanity.com/paper/2504.03329/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 24 of 24)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [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)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [Fast Timing-Conditioned Latent Audio Diffusion](https://mlsanity.com/paper/2402.04825/) (2024)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation](https://mlsanity.com/paper/2305.18474/) (2023)
- [Long-form music generation with latent diffusion](https://mlsanity.com/paper/2404.10301/) (2024)
- [Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2006.05822/) (2020)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Audio-Text Models Do Not Yet Leverage Natural Language](https://mlsanity.com/paper/2303.10667/) (2023)
- [Privacy-preserving Voice Analysis via Disentangled Representations](https://mlsanity.com/paper/2007.15064/) (2020)
- [SafeEar: Content Privacy-Preserving Audio Deepfake Detection](https://mlsanity.com/paper/2409.09272/) (2024)
- [Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition](https://mlsanity.com/paper/2408.09215/) (2024)
- [DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels](https://mlsanity.com/paper/2406.08056/) (2024)
- [Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation](https://mlsanity.com/paper/2410.17589/) (2024)
- [The impact of non-target events in synthetic soundscapes for sound event detection](https://mlsanity.com/paper/2109.14061/) (2021)
- [Synthetic training set generation using text-to-audio models for environmental sound classification](https://mlsanity.com/paper/2403.17864/) (2024)
- [Can Synthetic Audio From Generative Foundation Models Assist Audio Recognition and Speech Modeling?](https://mlsanity.com/paper/2406.08800/) (2024)
- [Towards Training Music Taggers on Synthetic Data](https://mlsanity.com/paper/2407.02156/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI-Assisted Music Production: A User Study on Text-to-Music Models](https://mlsanity.com/paper/2509.23364/) (2025)

## Abstract (arXiv)

This paper investigates the design of effective prompt strategies for generating realistic datasets using Text-To-Audio (TTA) models. We also analyze different techniques for efficiently combining these datasets to enhance their utility in sound classification tasks. By evaluating two sound classification datasets with two TTA models, we apply a range of prompt strategies. Our findings reveal that task-specific prompt strategies significantly outperform basic prompt approaches in data generation. Furthermore, merging datasets generated using different TTA models proves to enhance classification performance more effectively than merely increasing the training dataset size. Overall, our results underscore the advantages of these methods as effective data augmentation techniques using synthetic data.