Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910903507615744 |
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| author | Ronchini, Francesca Wu, Ho-Hsiang Lin, Wei-Cheng Antonacci, Fabio |
| author_facet | Ronchini, Francesca Wu, Ho-Hsiang Lin, Wei-Cheng Antonacci, Fabio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03329 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification Ronchini, Francesca Wu, Ho-Hsiang Lin, Wei-Cheng Antonacci, Fabio Audio and Speech Processing Artificial Intelligence Sound Signal Processing 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. |
| title | Mind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification |
| topic | Audio and Speech Processing Artificial Intelligence Sound Signal Processing |
| url | https://arxiv.org/abs/2504.03329 |