The avian dawn chorus presents a challenging opportunity to test autonomous recording units (ARUs) and associated recogniser software in the types of complex acoustic environments frequently encountered in the natural world. To date, extracting information from acoustic surveys using readily-available signal recognition tools (‘recognisers’) for use in biodiversity surveys has met with limited success. Combining signal detection methods used by different recognisers could improve performance, but this approach remains untested. Here, we evaluate the ability of four commonly used and commercially- or freely-available individual recognisers to detect species, focusing on five woodland birds with widely-differing song-types. We combined the likelihood scores (of a vocalisation originating from a target species) assigned to detections made by the four recognisers to devise an ensemble approach to detecting and classifying birdsong. We then assessed the relative performance of individual recognisers and that of the ensemble models. The ensemble models out-performed the individual recognisers across all five song-types, whilst also minimising false positive error rates for all species tested. Moreover, during acoustically complex dawn choruses, with many species singing in parallel, our ensemble approach resulted in detection of 74% of singing events, on average, across the five song-types, compared to 59% when averaged across the recognisers in isolation; a marked improvement. We suggest that this ensemble approach, used with suitably trained individual recognisers, has the potential to finally open up the use of ARUs as a means of automatically detecting the occurrence of target species and identifying patterns in singing activity over time in challenging acoustic environments.

禽类黎明合唱为在自然界中经常遇到的复杂声学环境中测试自主记录单元（ARU）和相关的识别器软件提供了一个具有挑战性的机会。迄今为止，使用现成的信号识别工具（“识别器”）从声学调查中提取信息用于生物多样性调查的成功有限。组合不同识别器使用的信号检测方法可以提高性能，但是这种方法仍未经测试。在这里，我们评估了四种常用的和商业上可用的或免费提供的个人识别器检测物种的能力，重点是五种歌曲类型迥异的林地鸟。我们结合了分配给四个识别器进行的检测的（从目标物种发声的）似然评分，以设计出一种整体方法来检测和分类鸟鸣。然后，我们评估了个体识别器和集成模型的相对性能。集成模型在所有五种歌曲类型中的表现均优于单个识别器，同时还使所有测试物种的误报率降至最低。此外，在声音复杂的黎明合唱中，许多物种并行唱歌，我们的合奏方法导致在五种歌曲类型中平均检测到74％的唱歌事件，而在单独的识别器中平均检测到59％；明显改善。我们建议与受过适当训练的个人识别器一起使用这种整体方法，