用信噪比分析神经网络检测动物叫声的性能，优化训练并估算叫声分布范围。
Acoustic evaluation of a neural network dedicated to the detection of animal vocalisations
- 通过合成信号信噪比与检出概率关系评估检测系统性能
- 实现检测距离建模，可估算叫声空间密度
- 适用于野外动物声学监测，尤其适合长期生态调查
长时间录音设备在复杂野外环境中的应用，推动了基于生态声学的大规模动物种群监测。当前自动信号检测方法多依赖机器学习指标评估，而声学层面的性能分析仍较少。针对岩雷鸟种群的声学监测，本文提出一种简单的声学评估方法：将合成信号的信噪比与其检出概率关联。该方法能揭示系统特性，优化训练过程，并实现检测距离建模，从而根据声学环境评估监测动态，估计叫声的空间密度。
原文摘要 · Abstract (English)
The accessibility of long-duration recorders, adapted to sometimes demanding field conditions, has enabled the deployment of extensive animal population monitoring campaigns through ecoacoustics. The effectiveness of automatic signal detection methods, increasingly based on neural approaches, is frequently evaluated solely through machine learning metrics, while acoustic analysis of performance remains rare. As part of the acoustic monitoring of Rock Ptarmigan populations, we propose here a simple method for acoustic analysis of the detection system's performance. The proposed measure is based on relating the signal-to-noise ratio of synthetic signals to their probability of detection. We show how this measure provides information about the system and allows optimisation of its training. We also show how it enables modelling of the detection distance, thus offering the possibility of evaluating its dynamics according to the sound environment and accessing an estimation of the spatial density of calls.
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