Categorization of Mouse Ultrasonic Vocalizations Using Machine Learning Techniques

A study of the ultrasonic vocalizations of several adult male BALB/c mice in the presence of a female, is undertaken in this study. A total of 179 distinct ultrasonic syllables referred to as “phonemes” are isolated, and in the resulting dataset, k-means and agglomerative clustering algorithms are implemented to group the ultrasonic vocalizations into clusters based on features extracted from their pitch contours. In order to find the optimal number of clusters, the elbow method was used, and nine distinct categories were obtained. Results when the k-means method was applied are presented through a matching matrix, while clustering results when the agglomerative technique was applied are presented as a dendrogram. The results of both methods are in line with the manual annotations made by the authors, as well as with the ones presented in the literature. The two methods of unsupervised analysis applied on 14 element feature vectors provide evidence that vocalizations can be grouped into nine clusters, which translates into the claim that there is a distinct repertoire of “syllables” or “phonemes”.