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Training Neural Audio Classifiers with Few Data
2019
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
doi:10.1109/icassp.2019.8682591
dblp:conf/icassp/PonsSS19
fatcat:lygsemjtevfw3czs23lmfhqlg4