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

Maximizing Audio Event Detection Model Performance on Small Datasets  Through Knowledge Transfer, Data Augmentation, And Pretraining: An Ablation  Study