Iterative knowledge distillation in r-cnns for weakly-labeled semi-supervised sound event detection
|K. Koutini, H. Eghbal-zadeh, G. Widmer. Iterative knowledge distillation in r-cnns for weakly-labeled semi-supervised sound event detection. pages 173-177, 11, 2018.|
|Buch||Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)|
In this paper, we present our approach used for the CP-JKU submission in Task 4 of the DCASE-2018 Challenge. We propose a novel iterative knowledge distillation technique for weakly-labeled semi-supervised event detection using neural networks, specifically Recurrent Convolutional Neural Networks (R-CNNs). R-CNNs are used to tag the unlabeled data and predict strong labels. Further, we use the R-CNN strong pseudo-labels on the training datasets and train new models after applying label-smoothing techniques on the strong pseudo-labels. Our proposed approach significantly improved the performance of the baseline, achieving the event-based f-measure of 40.86% compared to 15.11% event-based f-measure of the baseline in the provided test set from the development dataset.