Title: Acoustic evaluation of a neural network dedicated to the detection of animal vocalisations Show Chinese title

Title: 声学评估用于检测动物叫声的神经网络

Abstract: 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. Show Chinese abstract

Abstract: 长期记录器的可获得性，适应于有时苛刻的现场条件，使得通过生态声学部署广泛的动物种群监测活动成为可能。 自动信号检测方法的有效性，越来越多地基于神经方法，通常仅通过机器学习指标进行评估，而声学分析性能则很少见。 作为岩雷鸟种群声学监测的一部分，我们在这里提出了一种简单的检测系统性能声学分析方法。 所提出的度量方法是基于将合成信号的信噪比与其检测概率相关联。 我们展示了该度量如何提供有关系统的信息，并允许优化其训练。 我们还展示了它如何实现检测距离的建模，从而根据声音环境评估其动态，并获得叫声空间密度的估计。