Guided Learning Convolution System for DCASE 2019 Task 4

Abstract

In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments. We approach SED as a multiple instance learning (MIL) problem and employ a convolutional neural network (CNN) with class-wise attention pooling (cATP) module to solve it. By considering the interference caused by the co-occurrence of multiple events in the unbalanced dataset, we combine the cATP-MIL framework with the Disentangled Feature. To take advantage of the unlabeled data, we adopt Guided Learning for semi-supervised learning. A group of median filters with adaptive window sizes is utilized in post-processing. We also analyze the effect of the synthetic data on the performance of the model and finally achieve an event-based F-measure of 45.43% on the validation set and an event-based F-measure of 42.7% on the test set. The system we submitted to the challenge achieves the best performance compared to those of other participants.