  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.
