高效可靠检测鼠类超声鸣叫，自动分析速度提升百倍
A Reliable and Efficient Detection Pipeline for Rodent Ultrasonic Vocalizations
- 基于轮廓检测的自动化流程，不依赖复杂机器学习
- 在双数据集上平均精度、召回率等指标提升1.5倍以上
- 适合神经科学与行为学研究者快速分析超声信号
分析鼠类超声发声（USVs）对理解其情绪状态和社交行为至关重要，但人工分析耗时且易出错。已有自动检测系统多依赖机器学习，难以泛化至新数据集。为此，我们提出ContourUSV，一种高效自动化检测系统，包含谱图生成、清洗、预处理、轮廓检测、后处理及与人工标注对比评估。为验证鲁棒性，我们在公开的USVSEG数据集及本文发布的第二个数据集上，与三种前沿系统对比。平均而言，ContourUSV在两项数据集上分别实现精度提升1.51倍、召回率提升1.17倍、F1分数提升1.80倍、特异性提升1.49倍，同时平均加速117.07倍。
原文摘要 · Abstract (English)
Analyzing ultrasonic vocalizations (USVs) is crucial for understanding rodents' affective states and social behaviors, but the manual analysis is time-consuming and prone to errors. Automated USV detection systems have been developed to address these challenges. Yet, these systems often rely on machine learning and fail to generalize effectively to new datasets. To tackle these shortcomings, we introduce ContourUSV, an efficient automated system for detecting USVs from audio recordings. Our pipeline includes spectrogram generation, cleaning, pre-processing, contour detection, post-processing, and evaluation against manual annotations. To ensure robustness and reliability, we compared ContourUSV with three state-of-the-art systems using an existing open-access USV dataset (USVSEG) and a second dataset we are releasing publicly along with this paper. On average, across the two datasets, ContourUSV outperformed the other three systems with a 1.51x improvement in precision, 1.17x in recall, 1.80x in F1 score, and 1.49x in specificity while achieving an average speedup of 117.07x.
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