Thesis Defence: Highly Efficient Sound Classification for Marine Mammals

Xiangrui Liu, supervised by Dr. Julian Cheng, will defend their thesis titled “Highly Efficient Sound Classification for Marine Mammals” in partial fulfillment of the requirements for the degree of Master of Applied Science in Electrical Engineering.

An abstract for Xiangrui Liu’s thesis is included below.

Defences are open to all members of the campus community as well as the general public. Please email julian.cheng@ubc.ca to receive the Zoom link for this defence.

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ABSTRACT

Marine mammals and their ecosystem face significant threats from, for example, military active sonar and marine transportation. To mitigate this harm, early detection and classification of marine mammals are essential. Recent solutions involve spectrogram comparison and machine learning. However, the solutions show weaknesses in efficiency. Therefore, we propose a novel knowledge distillation framework, named XCFSMN, for this problem. We construct a teacher model that fuses the features extracted from an X-Vector extractor, a DenseNet, and a Cross-Covariance attended compact Feed-Forward Sequential Memory Network (cFSMN). The teacher model transfers knowledge to a simpler cFSMN model through a temperature-cooling strategy for efficient learning. Compared to multiple convolutional neural network backbones and transformers, the proposed framework achieves state-of-the-art efficiency and performance. The improved model size is approximately 25 times smaller and the inference time is 27 times shorter on average without affecting the model’s accuracy significantly.