Bird sound detection based on binarized convolutional neural networks

Bird Sound Detection (BSD) is helpful for monitoring biodiversity and in this regard, deep learning networks have shown good performance in BSD in recent years. However, such a complex network structure requires high memory resources and computing power at great cost for performing the extensive calculations required, which make it difficult to implement the hardware in BSD. Therefore, we designed an audio classification method for BSD using a Binarized Convolutional Neural Network (BCNN). The convolutional layers and fully connected layers of the original Convolutional Neural Network were binarized to two values. The Area Under ROC Curve (AUC) score of BCNN achieved comparable results with the CNN in an unseen evaluation. This paper proposes two networks (CNNs and BCNNs) for the BSD task of the IEEE AASP Challenge on the Detection and Classification of Acoustic Scenes and Events (DCASE2018). The Area Under ROC Curve (AUC) score of BCNN achieved comparable results with CNN on the unseen evaluation data. More importantly, the use of the BCNN could reduce the memory requirement and the hardware loss unit, which are of great significance to the hardware implementation of a bird sound detection system.