  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.
