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

Francesca Ronchini; Luca Comanducci; Fabio Antonacci

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arXiv · 2403.17864

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

Francesca Ronchini·Luca Comanducci·Fabio Antonacci

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Abstract

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.

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BibTeX RIS

Francesca Ronchini, Luca Comanducci, Fabio Antonacci. 2024-03-26. Synthetic training set generation using text-to-audio models for environmental sound classification. https://arxiv.org/abs/2403.17864

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