SM2+在手动和自动监测鬃毛狼声音中的应用
Abstract
Although bioacoustics is increasingly used to study species and environments for their monitoring and conservation, detecting calls produced by species of interest is prohibitively time consuming when done manually. Here we compared four methods for detecting and identifying roar-barks of maned wolves (Chrysocyon brachyurus) within long sound recordings: (1) a manual method, (2) an automated detector method using Raven Pro 1.4, (3) an automated detector method using XBAT and (4) a mixed method using XBAT’sdetector followed by manual verification. Recordings were done using a song meter installed at the Serra da Canastra National Park (Minas Gerais, Brazil). For each method we evaluated the following variables in a 24-h recording: (1) total time required analysing files, (2) number of false positives identified and (3) number of true positives identified compared to total number of target sounds. Automated methods required less time to analyse the recordings (77–93min) when compared to manual method (189min), but consistently presented more false positives and were less efficient in identifying true positives (manual ¼ 91.89%, Raven ¼ 32.43% and XBAT ¼ 84.86%). Adding a manual verification after XBAT detection dramatically increased efficiency in identifying target sounds (XBAT þ manual ¼ 100% true positives). Manual verification of XBAT detections seems to be the best way out of the proposed methods to collect target sound data for studies where large amounts of audio data need to be analysed in a reasonable time (111min, 58.73% of the time required to find calls manually).
摘要：
尽管生物声学越来越多地用于研究物种和环境以进行监测和保护，但手动检测感兴趣物种发出的叫声非常耗时。在这里，我们比较了四种在长录音中检测和识别鬃毛狼（Chrysocyon brachyurus）吼叫的方法：（1）手动方法，（2）使用Raven Pro 1.4的自动检测器方法，（3）使用XBAT的自动检测器法，以及（4）使用XBAT's检测器然后手动验证的混合方法。录音是使用安装在塞拉达卡纳斯特拉国家公园（巴西米纳斯吉拉斯州）的歌曲计完成的。对于每种方法，我们在24小时的记录中评估了以下变量：（1）分析文件所需的总时间，（2）识别的假阳性数量，以及（3）与目标声音总数相比识别的真阳性数量。与手动方法（189分钟）相比，自动方法需要更少的时间来分析记录（77-93分钟），但始终出现更多的假阳性，在识别真阳性方面效率较低（手动¼91.89%，Raven¼32.43%和XBAT¼84.86%）。在XBAT检测后添加手动验证大大提高了识别目标声音的效率（XBAT手动¼100%真阳性）。对于需要在合理时间内分析大量音频数据的研究，手动验证XBAT检测似乎是收集目标声音数据的最佳方法（111分钟，占手动查找呼叫所需时间的58.73%）。
关键词：SM2+，Wildlife Acoustics，野生动物声学监测，动物声学记录，自动声学监测
