Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection

Cemre Zor, Muhammad Awais, Josef Kittler, Miroslaw Bober, Sameed Husain, Qiuqiang Kong, Christian Kroos

Abstract: In this paper, we address the problem of bird audio detection and propose a new convolutional neural network architecture together with a divergence based information channel weighing strategy in order to achieve improved state-of-the-art performance and faster convergence. The effectiveness of the methodology is shown on the Bird Audio Detection Challenge 2018 (Detection and Classification of Acoustic Scenes and Events Challenge, Task 3) development data set.