SM2BAT+在声学调查中的自动化识别误差中的应用
Abstract
Assessing the state and trend of biodiversity in the face of anthropogenic threats requires large‐scale and long‐time monitoring, for which new recording methods offer interesting possibilities. Reduced costs and a huge increase in storage capac ity of acoustic recorders have resulted in an exponential use of passive acoustic monitoring (PAM) on a wide range of animal groups in recent years. PAM has led to a rapid growth in the quantity of acoustic data, making manual identification increasingly time‐consuming. Therefore, software detecting sound events, ex tracting numerous features and automatically identifying species have been de veloped. However, automated identification generates identification errors, which could influence analyses which look at the ecological response of species. Taking the case of bats for which PAM constitutes an efficient tool, we propose a cau tious method to account for errors in acoustic identifications of any taxa without excessive manual checking of recordings.
摘要：
面对人为威胁，评估生物多样性的状态和趋势需要大规模和长期的监测，新的记录方法为此提供了有趣的可能性。近年来，随着成本的降低和录音机存储容量的大幅增加，被动声学监测（PAM）在各种动物群体中的应用呈指数级增长。PAM导致声学数据量快速增长，使得人工识别越来越耗时。因此，开发了检测声音事件、提取大量特征和自动识别物种的软件。然而，自动识别会产生识别错误，这可能会影响对物种生态反应的分析。以PAM作为有效工具的蝙蝠为例，我们提出了一种谨慎的方法来解释任何分类群的声学识别错误，而无需对记录进行过多的人工检查。
关键词：SM2BAT+，生物声学、被动声学监测、半自动识别、调查方法
