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 memory and compute requirements are drastically reduced, and a tradeoff analysis of accuracy and complexity is presented. The analysis shows high accuracy at significantly reduced computational complexity compared to existing solutions. For example, a configuration with only 155k parameters and 49:3 million multiply adds per second is 81:75% exceeding human accuracy of 81:3% This improved efficiency can enable always on inference in energy efficient platforms.
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