Data from: Animal Sound Identifier (ASI): software for automated identification of vocal animals

Automated audio recording offers a powerful tool for acoustic monitoring schemes of bird, bat, frog and other vocal organisms, but the lack of automated species identification methods has made it difficult to fully utilise such data. We developed Animal Sound Identifier (ASI), a MATLAB software that performs probabilistic classification of species occurrences from field recordings. Unlike most previous approaches, ASI locates training data directly from the field recordings and thus avoids the need of pre‐defined reference libraries. We apply ASI to a case study on Amazonian birds, in which we classify the vocalisations of 14 species in 194 504 one‐minute audio segments using in total two weeks of expert time to construct, parameterise, and validate the classification models. We compare the classification performance of ASI (with training templates extracted automatically from field data) to that of monitoR (with training templates extracted manually from the Xeno‐Canto database), the results showing ASI to have substantially higher recall and precision rates.自动化音频录制为鸟类、蝙蝠、蛙类及其他发声生物的声学监测方案提供了强有力的工具，但自动化物种识别方法的缺失使得这类数据难以得到充分利用。我们开发了动物声音识别器（Animal Sound Identifier, ASI），这是一款可对野外录制音频中的物种出现情况进行概率分类的MATLAB软件。与绝大多数已有研究方法不同，ASI可直接从野外录制音频中提取训练数据，因此无需预先构建参考数据库。我们以亚马逊鸟类为研究对象开展案例研究，通过总计两周的专家工作时长完成分类模型的构建、参数化与验证工作，并对194504条一分钟音频片段中的14个物种的发声信号进行分类。我们将ASI（从野外数据中自动提取训练模板）的分类性能与monitoR（从Xeno-Canto数据库中手动提取训练模板）的分类性能进行对比，结果显示ASI的召回率与精确率均显著更高。