Training neural audio classifiers with few data

Pons J, Serrà J, Serra X. Training neural audio classifiers with few data. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

We investigate supervised learning strategies that improve the training of neural network audio classifiers on small annotated collections. In particular, we study whether (i) a naive regularization of the solution space, (ii) prototypical networks, (iii) transfer learning, or (iv) their combination, can foster deep learning models to better leverage a small amount of training examples. To this end, we evaluate (i-iv) for the tasks of acoustic event recognition and acoustic scene classification, considering from 1 to 100 labeled examples per class. Results indicate that transfer learning is a powerful strategy in such scenarios, but prototypical networks show promising results when one does not count with external or validation data.

https://ieeexplore.ieee.org/document/8682591

https://arxiv.org/abs/1810.10274

Slides http://jordipons.me/media/TrainingFewData_full.pdf

Github https://github.com/jordipons/neural-classifiers-with-few-audio