Audio-Based Automatic Giant Panda Behavior Recognition Using Competitive Fusion Learning

Automated giant panda (Ailuropoda melanoleuca) behavior recognition (GPBR) systems are highly beneficial for efficiently monitoring giant pandas in wildlife conservation missions. While video-based behavior recognition attracts a lot of attention, few studies have focused on audio-based methods. In this paper, we propose the exploitation of the audio data recorded by collar-mounted devices on giant pandas for the purpose of GPBR. We construct a new benchmark audio dataset of giant pandas named abPanda-5 for GPBR, which consists of 18,930 samples from five giant panda individuals with five main behaviors. To fully explore the bioacoustic features, we propose an audio-based method for automatic GPBR using competitive fusion learning. The method improves behavior recognition accuracy and robustness, without additional computational overhead in the inference stage. Experiments performed on the abPanda-5 dataset demonstrate the feasibility and effectiveness of our proposed method.