  An Xception model reaches state-of-the-art (SOTA) accuracy on the ESC-50
dataset for audio event detection through knowledge transfer from ImageNet
weights, pretraining on AudioSet, and an on-the-fly data augmentation pipeline.
This paper presents an ablation study that analyzes which components contribute
to the boost in performance and training time. A smaller Xception model is also
presented which nears SOTA performance with almost a third of the parameters.
