Graph Embedding with Mel-spectrograms for Underwater Acoustic Target Recognition

• URL: http://arxiv.org/abs/2512.11545v1
• Date: Fri, 12 Dec 2025 13:25:54 GMT
• Title: Graph Embedding with Mel-spectrograms for Underwater Acoustic Target Recognition
• Authors: Sheng Feng, Shuqing Ma, Xiaoqian Zhu,
• Abstract summary: The UATR-GTransformer is a non-Euclidean deep learning model that integrates Transformer architectures with graph neural networks (GNNs)<n>The proposed model effectively extracts rich frequency-domain information, highlighting its potential for applications in ocean engineering.<n> Experiments results based on two widely used benchmark datasets demonstrate that the UATR-GTransformer achieves performance competitive with state-of-the-art methods.
• Score: 3.4185611249587278
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Underwater acoustic target recognition (UATR) is extremely challenging due to the complexity of ship-radiated noise and the variability of ocean environments. Although deep learning (DL) approaches have achieved promising results, most existing models implicitly assume that underwater acoustic data lie in a Euclidean space. This assumption, however, is unsuitable for the inherently complex topology of underwater acoustic signals, which exhibit non-stationary, non-Gaussian, and nonlinear characteristics. To overcome this limitation, this paper proposes the UATR-GTransformer, a non-Euclidean DL model that integrates Transformer architectures with graph neural networks (GNNs). The model comprises three key components: a Mel patchify block, a GTransformer block, and a classification head. The Mel patchify block partitions the Mel-spectrogram into overlapping patches, while the GTransformer block employs a Transformer Encoder to capture mutual information between split patches to generate Mel-graph embeddings. Subsequently, a GNN enhances these embeddings by modeling local neighborhood relationships, and a feed-forward network (FFN) further performs feature transformation. Experiments results based on two widely used benchmark datasets demonstrate that the UATR-GTransformer achieves performance competitive with state-of-the-art methods. In addition, interpretability analysis reveals that the proposed model effectively extracts rich frequency-domain information, highlighting its potential for applications in ocean engineering.

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