QMUL bird audio detection challenge 2016

Cakir, Emre; Drossos, Konstantinos; Virtanen, Tuomas

In this paper, we focus on bird audio detection in short audio segments (namely 10 seconds) using stacked convolutional and recurrent neural networks. The evaluation data for this task was recorded in an acoustic soundscape different from the development data, thus motivating to work on methods that are generic and context independent. Data augmentation and regularization methods are proposed and evaluated in this regard. Area under curve (AUC) measure is used to compare different results. Our best achieved AUC measure on five cross-validations of the development data is 95.3% and 88.41% on the unseen evaluation data.


Bird audio detection; convolutional recurrent neural network