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Acoustic signal classification in underwater and air environments: leveraging transformer architecture and knowledge distillation
Thesis   Open access

Acoustic signal classification in underwater and air environments: leveraging transformer architecture and knowledge distillation

Quoc Thinh Vo
Master of Science (M.S.), Drexel University
Jun 2023
DOI:
https://doi.org/10.17918/00001744
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Abstract

Attention mechanism Knowledge distillation Electric transformers Underwater acoustics Underwater Acoustic Signal Classification Machine Learning Signal Processing
Identifying various acoustic sources can be challenging in both air and underwater settings. This is largely due to the difficulties in data collection, especially in ocean environments, and the variety of background noise they pose. Transformer architecture with self-attention mechanism has proven effective across various machine learning tasks, such as Natural Language Processing (NLP) and image recognition, even in challenging environments. This thesis presents the applications of Transformer in both air and underwater domains. In the air, we developed a Hierarchical Knowledge Distillation (H-KD) framework that utilized the Audio Spectrogram Transformer (AST) [1] architecture as a teacher model. The framework was designed using a multi-stage learning approach and cross-model integration to enhance performance of a low complexity Convolutional Neural Networks (CNN) student model. We applied it to an air acoustic dataset - TAU Urban Acoustic Scenes 2023 Mobile development [2] and demonstrated that it achieved a significantly improved performance compared to the Baseline system [3]. After verifying the model on publicly available datasets, our proposed model was submitted to the 2023 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2023) [3]. In the underwater acoustic signals experiment, we modified and adapted the Hierarchical Token Semantic - Audio Transformer (HTS-AT) [4] architecture to make it better suited underwater environments. We applied it to an underwater acoustic dataset - shipsEar [5] and demonstrated that our proposed method outperformed some of the latest classification methods. The promising results obtained motivate further exploration of Transformer-based approaches in future research to advance the state-of-the-art (SOTA) in Acoustic Signal Classifications (ASC).

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