系统测试声学枪击分类特征提取方法，发现参数选择显著影响识别准确率。
Exploring Feature Extraction Technique Parameters for Acoustic Gunshot Classification
- 对比三种特征提取方法在23,000条枪击录音上的表现
- 选对参数可使准确率提升4.7%，整体最高提升20%
- 适合关注实际部署中模型泛化能力的研究者
声学枪击检测在民用公共安全、军事行动和野生动物保护中有广泛应用，但现有研究缺乏对特征提取技术的严谨探索，尤其在真实数据上的泛化能力。商业枪击检测系统效果参差不齐，表明该问题尚未得到充分解决。本文基于包含85种枪支、21种口径的23,000条枪击录音数据集，系统评估了三种常见特征提取技术，共12组不同参数配置，使用ResNet-18进行基准测试。结果表明，采用正确的特征提取方法可将顶1准确率提升高达20%，而针对特定方法优化参数可再提升4.7%。研究揭示了特征工程在实际应用中的关键作用。
原文摘要 · Abstract (English)
Acoustic gunshot detection is a problem with applications across civilian public safety, military operations, and wildlife conservation, yet the field lacks a rigorous exploration of feature extraction techniques with a focus on generalization to realistic data. The mixed effectiveness of commercial gunshot detection and classification systems indicates an open problem that is not adequately addressed by the current literature. In this paper, we present a systematic investigation of common feature extraction techniques using a dataset of 23,000 gunshot recordings across 85 firearms and 21 calibers. We benchmark three feature extraction techniques with 12 total unique parameter sets using ResNet-18. Our results demonstrate that using the correct feature extraction technique can improve top-1 accuracy by up to 20%, and utilizing the correct parameters for a given feature extraction technique can improve that value by up to 4.7%.
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