迈向可部署的水下船只分类

中文摘要 AI 辅助

我们提出了一种紧凑的水下声学分类框架，该框架结合了多表示特征工程、时间统计池化和紧凑卷积架构，专为声学时频和耳蜗表示设计。我们研究了多种传统和听觉启发的表示，并首先在ShipsEar数据集上评估了轻量级分类器和常规神经网络（CNNs）。在提供的划分上，一个两层CNN实现了宏F1为0.9918，而径向基函数支持向量机（RBF-SVM）达到了0.9883。然而，源录音的来源无法重建，阻碍了录音无关泛化的验证。因此，我们在DeepShip数据集上使用录音级划分（先划分再分段）进行评估。在此协议下，一个157K参数的紧凑CNN在测试集上实现了宏F1为0.7226，而一个11.17M参数的ResNet18在匹配设置下并未提升验证性能。这些结果证明了表示感知的特征和模型设计以及严格的录音级评估对于紧凑水下声学系统的分类性能和可部署性的重要性。

英文摘要

We propose a compact underwater acoustic classification framework combining multi-representation feature engineering, temporal statistical pooling, and compact convolutional architectures designed for acoustic time-frequency and cochlear representations. We investigate multiple conventional and auditory-inspired representations and first evaluate lightweight classifiers and Conventional Neural Networks (CNNs) on ShipsEar dataset. On the provided split, a two-layer CNN achieves a macro F1 of 0.9918, while a Radial Basis Function Support Vector Machine (RBF-SVM) reaches 0.9883. However, source-recording provenance cannot be reconstructed, preventing verification of recording-independent generalisation. We therefore evaluate on DeepShip dataset using recording-level partitioning before segmentation. Under this protocol, a 157K-parameter compact CNN achieves a test macro F1 of 0.7226, while an 11.17M-parameter ResNet18 provides no improvement in validation performance under the matched setting. These results demonstrate the importance of representation-aware feature and model design, together with rigorous recording-level evaluation, for classification performance and deployability in compact underwater acoustic systems.

发表机构

• Western Sydney University（西悉尼大学）

机构由 AI 辅助整理，请以论文原文为准。