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We propose an efficient end-to-end convolutional neural network architecture,AclNet, for audio classification. When trained with our data augmentation andregularization, we achieved state-of-the-art performance on the ESC-50 corpuswith 85:65% accuracy. Our network allows configurations such that memory andcompute requirements are drastically reduced, and a tradeoff analysis ofaccuracy and complexity is presented. The analysis shows high accuracy atsignificantly reduced computational complexity compared to existing solutions.For example, a configuration with only 155k parameters and 49:3 millionmultiply-adds per second is 81:75%, exceeding human accuracy of 81:3%. Thisimproved efficiency can enable always-on inference in energy-efficientplatforms.

AclNet: efficient end-to-end audio classification CNN