Habitat and reproduction surveys of raptors are common components of environmental impact assessments. Raptors are few in number and widely dispersed; therefore, raptor surveys require greater survey frequency compared to surveys for many other bird species, which, in turn, may result in extra costs as well as negative impact on raptors caused by observers. In this research, we propose a new, efficient, method for surveying raptors which is low-impact on the raptors themselves. The target species of this study was the Northern goshawk (Accipiter gentilis), categorized as “Near Threatened” in the Japanese Red List, its population estimated at 5010–8950 in 2008. We developed a system which can automatically classify five classes of sounds, including goshawk calls, using a convolutional neural network. To establish the system's applicability as a survey method, we then additionally verified three factors; (1) applicability of the method in different locales, (2) optimal distance from the nest to recording device, and (3) the ability to gauge reproductive states. We report an overall accuracy of 97.0% for this system. This system could classify, with high accuracy, goshawk calls “kek-kek-kek” and “whee-oo” across different locales. This system could classify sounds collected by recorders placed far from nests and within the forest, not placed on the nest itself. Some limitations of the system, notably limitation in data for verification, remain to be improved through further studies. This survey method can be used to judge whether an area was inhabited by goshawks or not, and their approximate reproductive state, based on a three-hour recording of environmental sounds, with required fieldwork limited to the placing and collection of recorders, and minimal additional human input, due to the system classifying the sounds automatically.

猛禽的生境和繁殖调查是环境影响评估的常见组成部分。猛禽数量很少，分布广泛。因此，与对许多其他鸟类进行的调查相比，猛禽调查所需要的调查频率更高，这反过来可能会导致额外的费用以及观察员对猛禽的负面影响。在这项研究中，我们提出了一种新的，有效的方法来测量猛禽，该方法对猛禽本身的影响很小。这项研究的目标物种是北方苍鹰（Accipiter gentilis），在“日本红色名录”中被归类为“近乎威胁”，其人口在2008年估计为5010至8950。我们开发了一种系统，该系统可以使用卷积神经网络自动分类五种声音，包括苍鹰​​声。为了确定该系统作为一种调查方法的适用性，我们另外验证了三个因素：（1）该方法在不同地区的适用性；（2）从巢到记录设备的最佳距离；以及（3）测量生殖状态的能力。我们报告此系统的整体准确性为97.0％。该系统可以高精度地在不同地区对苍鹰叫“ kek-kek-kek”和“ whee-oo”进行分类。该系统可以对远离巢穴且位于森林内而不是置于巢穴本身上的录音机收集的声音进行分类。系统的一些局限性，验证数据的局限性，有待进一步研究加以改进。这种调查方法可用于根据一个三小时的环境声音记录来判断一个地区是否有苍鹰居住，以及它们的大致生殖状态，所需的实地调查仅限于放置和收集记录器，并且最少的附加费用人为输入，因为系统会自动对声音进行分类。