Sound Event Detection and Direction of Arrival Estimation using Residual Net and Recurrent Neural Networks

Abstract

This paper presents deep learning approach for sound events detection and localization, which is also a part of detection and classification of acoustic scenes and events (DCASE) challenge 2019 Task 3. Deep residual nets originally used for image classification are adapted and combined with recurrent neural networks (RNN) to estimate the onset-offset of sound events, sound events class, and their direction in a reverberant environment. Additionally, data augmentation and post processing techniques are applied to generalize and improve the system performance on unseen data. Using our best model on validation dataset, sound events detection achieves F1-score of 0.89 and error rate of 0.18, whereas sound source localization task achieves angular error of 8° and 90% frame recall.