The use of Artificial Neural Networks in studying lemur vocal communication

Previous studies have applied Arti fi cial Neural Networks (ANNs) successfully to bioacoustic problems at different levels of analysis (individual and species identi fi cation, vocal repertoire categorization, and analysis of sound structure) but not to nonhuman primates. Here, we report the results of applying this tool to two important problems in primate vocal communication. First, we apply a supervised ANN to classify 222 long grunt vocalizations emitted by fi ve species of the genus Eulemur. Second, we use an unsupervised self-organizing network to identify discrete categories within the vocal repertoire of black lemurs ( Eulemur macaco ). Calls were characterized by both spectral (fundamental frequency and formants) and temporal features. The result show not only that ANNs are effective for studying primate vocalizations but also that this tool can increase the ef fi ciency, objectivity, and biological signi fi cance of vocal classi fi cation greatly. The advantages of ANNs over more commonly used statistical techniques and different applications for supervised and unsupervised ANNs are discussed.