TrellisNet-Based Architecture for Sound Event Localization and Detection with Reassembly Learning

Abstract

This paper proposes a deep learning technique and network model for DCASE 2019 task 3: Sound Event Localization and Detection. Currently, the convolutional recurrent neural network is known as the state-of-the-art technique for sound classification and detection. We focus on proposing TrellisNet-based architecture that can replace the convolutional recurrent neural network. Our TrellisNet-based architecture has better performance in the direction of arrival estimation compared to the convolutional recurrent neural network. We also propose reassembly learning to design a single network that handles dependent sub-tasks together. Reassembly learning is a method to divide multi-task into individual sub-tasks, to train each sub-task, then reassemble and fine-tune them into a single network. Experimental results show that the proposed method improves sound event localization and detection performance compared to the DCASE 2019 baseline system.