提出新方法与评估协议，实现水下船体跨航次声纹识别。
Open-Set Vessel Re-Identification from Underwater Ship-Radiated Noise with a Raw-Waveform Selective-Kernel Acoustic Neural Network (SKANN) and a Cross-Passage Evaluation Protocol
- 设计原始波形神经网络SKANN，融合多尺度滤波与选择性注意力机制。
- 跨航次识别rank-1达0.26，融合方法提升至0.35，显著优于单一模型。
- 公开数据集与代码，适合水下目标识别与系统评估研究者使用。
水下声学目标识别长期聚焦于按船型的闭集分类，无法判断监测系统是否曾听见过该船体。本文首次在公开水听器数据上形式化开放集、跨航次船体重识别任务，并提出一套评估协议，消除因船舶编号分离、航次分离、源纯净样本及音频校验去重等导致的高分误导。提出SKANN模型，其前端为四尺度可学习滤波器组，通过选择性核注意力融合，采用角间距损失与扰动录音链、环境噪声和多径传播的增强策略，保留携带身份信息的窄带线特征。在40艘船的IARA画廊（96个查询，98个航次候选）上，嵌入表示rank-1为0.25，自动窄带音调比较器为0.26，二者统计无差异，但嵌入排序整体更可靠（AUC 0.82 vs 0.76），融合后达到rank-1 0.35，唯一具名义显著性的对比，体现部分互补性。仅去重操作就消除了16-21点的虚假性能优势，超过任何方法间差异。进一步发现：ShipsEar无法在身份协议下区分船体与记录通道；跨网络微调仅对微调时见过的船有效，对未见船无效。结果支持分析师基于排序列表进行筛选，而非精确识别。检查点、验证嵌入、航行图与每查询输出已开源（CC-BY-4.0，doi:10.5281/zenodo.22160138）。
原文摘要 · Abstract (English)
Underwater acoustic target recognition has converged on closed-set classification by vessel type, a task that does not answer whether a monitoring system has heard this hull before. We formalise open-set, cross-passage vessel re-identification on public hydrophone data and specify a protocol that removes the two easiest routes to a high score: hull-disjoint splits keyed to MMSI/IMO, galleries and queries from disjoint passages of each hull, source-pure galleries, and an audio-adjudicated transit-deduplication gate. We describe SKANN, a raw-waveform encoder whose front end is a four-scale bank of learned filters fused by selective-kernel attention, trained with an angular-margin objective and an augmentation regime that perturbs recording chain, ambient noise and multipath while preserving the narrowband lines that carry identity. On a 40-hull IARA gallery (96 queries, 98 passage candidates), cross-passage rank-1 is 0.25 for the embedding and 0.26 for an automated narrowband-tonal comparator; the two are statistically indistinguishable at the top of the ranking, the embedding orders the rest of the list more reliably (AUC 0.82 vs 0.76), and their score fusion reaches rank-1 0.35 -- the only contrast that attains nominal significance, presented as evidence of partial complementarity, not as a recommendation. Transit deduplication alone removes a 16-21 point apparent rank-1 advantage, larger than any between-method difference. Two further findings delimit what public data can support: ShipsEar cannot separate hull identity from recording channel under an identity protocol, and cross-network fine-tuning helps vessels seen during fine-tuning but is a null result on unseen ones. The results support analyst triage over a ranked shortlist, not identification. Checkpoint, validation embeddings, transit map and per-query outputs are released under CC-BY-4.0 (doi:10.5281/zenodo.22160138).
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