Synthetic training set generation using text-to-audio models for environmental sound classification
<|reference_start|>Synthetic training set generation using text-to-audio models for environmental sound classification: 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.<|reference_end|>
@article{ronchini2024synthetic,
    title={Synthetic training set generation using text-to-audio models for
  environmental sound classification},
    author={Francesca Ronchini, Luca Comanducci, Fabio Antonacci},
    journal={arXiv preprint arXiv:2403.17864},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17864},
    primaryClass={eess.AS cs.SD eess.SP}
}