声学特征与YAMNet嵌入融合提升无人机音频检测，并提出防泄漏评估协议
Hybrid acoustic and pretrained embedding feature fusion for unmanned aerial vehicle detection using stacking ensemble learning under a leakage-aware evaluation protocol
无人机音频检测的论文，发现常用数据集随机划分会虚高约2-3个点准确率，换成按来源划分的协议后测得更真实的成绩。
该研究基于公开的 DroneAudioDataset（11,704 条一秒音频）研究多旋翼无人机的声学检测。作者指出该数据集中 96.4% 的非无人机片段来自 ESC-50，随机划分会让同一来源同时出现在训练与测试集，导致准确率被高估 1.89 到 3.22 个百分点。他们提出按来源分组的五折交叉验证协议，并用 57 个手工声学描述符融合 YAMNet 的 1024 维嵌入，通过 Random Forest 与 XGBoost 组成 stacking 集成。在新协议下模型达到 95.29% 平均准确率、0.990 的 ROC AUC，单 CPU 线程推理耗时 51.0 毫秒。特征选择方面，混合方法用 16-18 个特征即可匹配全部 44 个基线特征的准确率。
Hybrid acoustic and pretrained embedding feature fusion for unmanned aerial vehicle detection using stacking ensemble learning under a leakage-aware evaluation protocol
Small multirotor drones produce a characteristic acoustic signature, and microphones offer a low-cost way to detect them. High accuracies have been reported on public drone audio benchmarks, often with random clip-level splits of datasets built from a few long recordings and pieces of existing sound collections, so pieces of one source can appear in both training and test data. We study the binary task of the public DroneAudioDataset (11,704 one-second clips). Its 1,332 drone clips come from 257 five-second chunks of 7 recordings, and 96.4% of its non-drone clips are pieces of the 2,000 clips of the ESC-50 sound dataset. A grouped, source-disjoint protocol (five folds, three repeats) is compared with a random split. Each clip is described by 57 hand-crafted acoustic descriptors, reduced by a hybrid minimum-redundancy maximum-relevance and ReliefF selection or by NSGA-II multi-objective search, and by a 1024-dimensional embedding from the pretrained YAMNet network. Random Forest and XGBoost learners on both views form a stacking ensemble. Under the grouped protocol, the two-view stack reached a mean accuracy of 95.29% (SD 1.62 points over 15 test folds), a specificity of 98.29% and an area under the ROC curve of 0.990, and it correctly classified 98.78% of unseen ESC-50 source clips as non-drone. The random split overstated accuracy by 1.89 to 3.22 points for the four models. Adding YAMNet embeddings to the hand-crafted view raised accuracy by 1.11 points (higher in 12 of 15 folds). Hybrid selection matched the accuracy of all 44 baseline descriptors with 16 to 18 features, and NSGA-II retained 4 to 6 features at lower accuracy. The stack took 51.0 ms per clip on one CPU thread. Source-disjoint evaluation is needed for a fair benchmark on this dataset, and fusing hand-crafted descriptors with pretrained embeddings gives a small but consistent gain at modest computational cost.