AclNet: efficient end-to-end audio classification CNN

We propose an efficient end-to-end convolutional neural network architecture, AclNet, for audio classification. When trained with our data augmentation and regularization, we achieved state-of-the-art performance on the ESC-50 corpus with 85:65% accuracy. Our network allows configurations such that...

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Veröffentlicht in:	arXiv.org 2018-11
Hauptverfasser:	Huang, Jonathan J, Alvarado Leanos, Juan Jose
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Artificial neural networks Audio data Classification Complexity Computer memory Configurations Neural networks Regularization
Online-Zugang:	Volltext
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