Khaled Koutini, Hamid Eghbal-Zadeh, Gerhard Widmer,
"Iterative Knowledge Distillation in R-CNNS for Weakly-Labeled Semi-Supervised Sound Event Detection"
: Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018), 11-2018
Original Titel:
Iterative Knowledge Distillation in R-CNNS for Weakly-Labeled Semi-Supervised Sound Event Detection
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)
Original Kurzfassung:
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.
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