ABSTRACT Automated ultrasonic recordings are widely used in basic and applied research to detect the presence of bats. Often, algorithms for the automated identification of species are based on a pre-processing of acoustic information that involves the generation of spectrograms. Even though this approach is technically advanced, recent surveys highlight substantially high failure rates to identify species correctly, which urges for improved processes. Here, we tested an entirely new method, in particular, the transformation of ultrasonic recordings into variant maps. To compare this method with a spectrogram-based method, we used a database consisting of 160 echolocation calls from eight European bat species, including species of the genus Myotis that are inherently difficult to separate based on echolocation calls. For non-Myotis species, both methods led to a 100% correct identification rate, while for Myotis species the use of variant maps led to a lower identification rate of 85.3% compared to 91.1% that was achieved with a spectrogram-based method. However, a combination of both methods could lead to an identification rate of 94.1% for Myotis species. This result suggests combining our approach with spectrogram-based techniques to improve the automated identification of species based on acoustic information.

中文翻译：

---

蝙蝠回声定位呼叫识别算法的变体图

摘要 自动超声波记录广泛用于基础和应用研究，以检测蝙蝠的存在。通常，用于自动识别物种的算法基于声学信息的预处理，包括生成频谱图。尽管这种方法在技术上是先进的，但最近的调查强调正确识别物种的失败率非常高，这迫切需要改进流程。在这里，我们测试了一种全新的方法，特别是将超声波记录转换为变体图。为了将此方法与基于频谱图的方法进行比较，我们使用了一个数据库，该数据库由来自八个欧洲蝙蝠物种的 160 个回声定位调用组成，其中包括根据回声定位调用本质上难以分离的 Myotis 属物种。对于非肌炎物种，两种方法都实现了 100% 的正确识别率，而对于 Myotis 物种，使用变异图导致的识别率较低，为 85.3%，而基于谱图的方法实现的识别率为 91.1%。然而，这两种方法的结合可以使 Myotis 物种的识别率达到 94.1%。该结果表明将我们的方法与基于频谱图的技术相结合，以改进基于声学信息的物种自动识别。