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