Building a robust underwater acoustic recognition system in real-world scenarios is challenging due to the complex underwater environment and the dynamic motion states of targets. A promising optimization approach is to leverage the intrinsic physical characteristics of targets, which remain invariable regardless of environmental conditions, to provide robust insights. However, our study reveals that while physical characteristics exhibit robust properties, they may lack class-specific discriminative patterns. Consequently, directly incorporating physical characteristics into model training can potentially introduce unintended inductive biases, leading to performance degradation. To utilize the benefits of physical characteristics while mitigating possible detrimental effects, we propose DEMONet in this study, which utilizes the detection of envelope modulation on noise (DEMON) to provide robust insights into the shaft frequency or blade counts of targets. DEMONet is a multi-expert network that allocates various underwater signals to their best-matched expert layer based on DEMON spectra for fine-grained signal processing. Thereinto, DEMON spectra are solely responsible for providing implicit physical characteristics without establishing a mapping relationship with the target category. Furthermore, to mitigate noise and spurious modulation spectra in DEMON features, we introduce a cross-temporal alignment strategy and employ a variational autoencoder (VAE) to reconstruct noise-resistant DEMON spectra to replace the raw DEMON features. The effectiveness of the proposed DEMONet with cross-temporal VAE was primarily evaluated on the DeepShip dataset and our proprietary datasets. Experimental results demonstrated that our approach could achieve state-of-the-art performance on both datasets.

翻译：在实际场景中构建稳健的水下声学识别系统具有挑战性，这主要源于复杂的水下环境以及目标的动态运动状态。一种具有前景的优化方法是利用目标固有的物理特性，这些特性不随环境条件变化，能够提供稳健的洞察。然而，我们的研究表明，尽管物理特性展现出稳健的属性，但它们可能缺乏类别特异性的判别模式。因此，直接将物理特性纳入模型训练可能会引入非预期的归纳偏置，导致性能下降。为了在利用物理特性优势的同时减轻其可能的不利影响，本研究提出了DEMONet，该模型利用噪声包络调制检测（DEMON）为目标轴频或叶片数提供稳健的洞察。DEMONet是一种多专家网络，它基于DEMON谱将不同的水下信号分配至其最佳匹配的专家层进行细粒度信号处理。其中，DEMON谱仅负责提供隐式的物理特性，而不与目标类别建立映射关系。此外，为了减轻DEMON特征中的噪声和虚假调制谱，我们引入了一种跨时序对齐策略，并采用变分自编码器（VAE）来重建抗噪声的DEMON谱以替代原始DEMON特征。所提出的结合跨时序VAE的DEMONet的有效性主要在DeepShip数据集及我们专有的数据集上进行了评估。实验结果表明，我们的方法在两个数据集上均能达到最先进的性能。