Abstract:
In this paper, we present our system of sound event detection and separation in domestic environments for DCASE 2020. The task aims to determine which sound events appear in a clip and the detailed temporal ranges they occupy. The system is trained by using weakly-labeled and unlabeled real data and synthetic data with strongly annotated labels. Our proposed model structure includes a feature-level front-end based on convolution neural networks (CNN), followed by both embedding-level and instance-level back-end attention modules. In order to make full use of the large amount of unlabeled data, we jointly adopt the Guided Learning and Mean Teacher approaches to carry out weakly-supervised learning and semi-supervised learning. In addition, a set of adaptive median windows for individual sound events is used to smooth the frame-level predictions in post-processing. In the public evaluation set of DCASE 2019, the best event-based <i>F</i><sub>1</sub>-score achieved by our system is 48.50%, which is a relative improvement of 27.16% over the official baseline (38.14%). In addition, in the development set of DCASE 2020, our best system also achieves a relative improvement of 32.91% over the baseline (45.68% vs. 34.37%)
