Dipartimento di Biologia Animale e dell'Uomo, Università di Torino, Italy.
Am J Primatol. 2010 Apr;72(4):337-48. doi: 10.1002/ajp.20786.
The identification of the vocal repertoire of a species represents a crucial prerequisite for a correct interpretation of animal behavior. Artificial Neural Networks (ANNs) have been widely used in behavioral sciences, and today are considered a valuable classification tool for reducing the level of subjectivity and allowing replicable results across different studies. However, to date, no studies have applied this tool to nonhuman primate vocalizations. Here, we apply for the first time ANNs, to discriminate the vocal repertoire in a primate species, Eulemur macaco macaco. We designed an automatic procedure to extract both spectral and temporal features from signals, and performed a comparative analysis between a supervised Multilayer Perceptron and two statistical approaches commonly used in primatology (Discriminant Function Analysis and Cluster Analysis), in order to explore pros and cons of these methods in bioacoustic classification. Our results show that ANNs were able to recognize all seven vocal categories previously described (92.5-95.6%) and perform better than either statistical analysis (76.1-88.4%). The results show that ANNs can provide an effective and robust method for automatic classification also in primates, suggesting that neural models can represent a valuable tool to contribute to a better understanding of primate vocal communication. The use of neural networks to identify primate vocalizations and the further development of this approach in studying primate communication are discussed.
物种的声音曲目识别是正确解释动物行为的关键前提。人工神经网络（ANNs）已广泛应用于行为科学，并且如今被认为是一种有价值的分类工具，可以降低主观性水平，并允许在不同的研究中获得可重复的结果。然而，迄今为止，尚无研究将此工具应用于非人类灵长类动物的发声。在这里，我们首次将人工神经网络（ANNs）应用于灵长类物种——马岛长尾狸猫（Eulemur macaco macaco）的声音曲目识别。我们设计了一种自动程序，从信号中提取光谱和时间特征，并对监督型多层感知机和两种在灵长类动物学中常用的统计方法（判别函数分析和聚类分析）进行了比较分析，以探讨这些方法在生物声学分类中的优缺点。我们的结果表明，ANNs 能够识别之前描述的所有七种声音类别（92.5-95.6%），并且比任何统计分析（76.1-88.4%）的效果都要好。结果表明，ANNs 可以为自动分类提供一种有效且稳健的方法，即使在灵长类动物中也是如此，这表明神经模型可以作为一种有价值的工具，有助于更好地理解灵长类动物的声音通讯。讨论了使用神经网络识别灵长类动物的发声以及在研究灵长类动物通讯方面进一步开发这种方法的问题。
