Virtual Adversarial Training and Data Augmentation for Acoustic Event Detection with Gated Recurrent Neural Networks

In this paper, we use gated recurrent neural networks (GRNNs) for efficiently detecting environmental events of the IEEE Detection and Classification of Acoustic Scenes and Events challenge (DCASE2016). For this acoustic event detection task data is limited. Therefore, we propose data augmentation such as on-the-fly shuffling and virtual adversarial training for regularization of the GRNNs. Both improve the performance using GRNNs. We obtain a segment-based error rate of 0.59 and an F-score of 58.6%.