This letter presents ShuffleFAC, a lightweight acoustic model for ship-radiated sound classification in resource-constrained maritime monitoring systems. ShuffleFAC integrates Frequency-Aware convolution into an efficiency-oriented backbone using separable convolution, point-wise group convolution, and channel shuffle, enabling frequency-sensitive feature extraction with low computational cost. Experiments on the DeepShip dataset show that ShuffleFAC achieves competitive performance with substantially reduced complexity. In particular, ShuffleFAC ($γ=16$) attains a macro F1-score of 71.45 $\pm$ 1.18% using 39K parameters and 3.06M MACs, and achieves an inference latency of 6.05 $\pm$ 0.95ms on a Raspberry Pi. Compared with MicroNet0, it improves macro F1-score by 1.82 % while reducing model size by 9.7x and latency by 2.5x. These results indicate that ShuffleFAC is suitable for real-time embedded UATR.

翻译：本文提出ShuffleFAC，一种用于资源受限海洋监测系统中船舶辐射噪声分类的轻量级声学模型。ShuffleFAC将频率感知卷积集成到一个以效率为导向的主干网络中，该主干网络采用可分离卷积、逐点分组卷积和通道混洗技术，从而能够以较低的计算成本实现频率敏感的特征提取。在DeepShip数据集上的实验表明，ShuffleFAC在显著降低复杂度的同时，获得了有竞争力的性能。具体而言，ShuffleFAC ($γ=16$) 以39K参数和3.06M MACs的代价，取得了71.45 $\pm$ 1.18%的宏F1分数，并在树莓派上实现了6.05 $\pm$ 0.95ms的推理延迟。与MicroNet0相比，其宏F1分数提高了1.82%，同时模型尺寸减少了9.7倍，延迟降低了2.5倍。这些结果表明ShuffleFAC适用于实时嵌入式UATR。