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A method for automated individual, species and call type recognition in free-ranging animals

机译：一种自动放养动物的个体，种类和呼叫类型识别方法

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The ability to identify individuals reliably is often a key prerequisite for animal behaviour studies in the wild. In primates, recognition of other group members can be based on individual differences in the voice, but these cues are typically too subtle for human observers. We applied a combined mechanism consisting of a call feature extraction (mel frequency cepstral coefficients) and pattern recognition algorithm (artificial neural networks) to investigate whether automated caller identification is possible in free-ranging primates. The mechanism was tested for its accuracy in recognizing species, call type and caller identity in a large population of free-ranging blue monkeys, Cercopithecus mitis stuhlmanni, in Budongo Forest, Uganda. Classification was highly accurate with 96% at the species, 98% at the call type and 73% at the caller level. It also outperformed conventional discriminant function analysis in the individual recognition task. We conclude that software based on this method will make a powerful tool for future animal behaviour research, as it allows for automatic, fast and objective classifications in different animal species.
机译：可靠地识别个人的能力通常是野外动物行为研究的关键前提。在灵长类动物中，对其他小组成员的识别可以基于声音的个体差异，但是这些提示对于人类观察者来说通常太微妙了。我们应用了一种由呼叫特征提取（梅尔频率倒谱系数）和模式识别算法（人工神经网络）组成的组合机制，以研究在自由放养的灵长类动物中是否可能进行自动呼叫者识别。测试了该机制在识别乌干达Budongo森林中大量散养蓝猴Cercopithecus mitis stuhlmanni的物种，呼叫类型和呼叫者身份方面的准确性。分类非常准确，其中物种占96％，呼叫类型占98％，呼叫者级别占73％。它在个人识别任务中也胜过传统的判别函数分析。我们得出结论，基于此方法的软件将为未来的动物行为研究提供强大的工具，因为它可以对不同动物物种进行自动，快速和客观的分类。

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