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arXiv · 1810.06897

Sound event detection using weakly-labeled semi-supervised data with GCRNNS, VAT and Self-Adaptive Label Refinement

Robert Harb·Franz Pernkopf
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Abstract

In this paper, we present a gated convolutional recurrent neural network based approach to solve task 4, large-scale weakly labelled semi-supervised sound event detection in domestic environments, of the DCASE 2018 challenge. Gated linear units and a temporal attention layer are used to predict the onset and offset of sound events in 10s long audio clips. Whereby for training only weakly-labelled data is used. Virtual adversarial training is used for regularization, utilizing both labelled and unlabeled data. Furthermore, we introduce self-adaptive label refinement, a method which allows unsupervised adaption of our trained system to refine the accuracy of frame-level class predictions. The proposed system reaches an overall macro averaged event-based F-score of 34.6%, resulting in a relative improvement of 20.5% over the baseline system.

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Robert Harb, Franz Pernkopf. 2018-10-16. Sound event detection using weakly-labeled semi-supervised data with GCRNNS, VAT and Self-Adaptive Label Refinement. https://arxiv.org/abs/1810.06897

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