Deep learning for hydrophone big data

This paper presents an efficient deep learning framework for long-term monitoring of acoustic events from hydrophone big data. The large-scale noisy ONC (Ocean Networks Canada) data may contain rare acoustic events, which can be automatically recognized by utilizing a deep convolutional neural network. Few works have been reported in the area of deep learning for the recognition of different kinds of marine mammal calls which however is crucial for many applications such as marine navigation. In this proposed scheme, deep learning feature sets are adopted and processed by a support vector machione (SVM) classifier. The proposed method is tested with 28685 minutes of data, spanning a single year with 5573 whale calls/acoustic events, and using a human operator's annotations. The experimental results show that the average accuracy rate of recognition using deep feature learning are 98.69% (two-class) and 94.48% (multi-class), respectively, for the proposed recognition scheme, which outperforms the MFCC-based method.