Classifying sex and strain from mouse ultrasonic vocalizations using deep learning
Fig 1
Recording and classifying mouse vocalizations.
A Mouse vocalization were recorded from a pair of mice, in which one was awake, while the other was anesthetized, allowing an unambiguous attribution of the recorded vocalizations. B Vocalization from male and female mice (recorded in separate sessions) share a lot of properties, while differing in others. The present samples were picked at random and indicate that differences exist, while other samples would look more similar. C Vocalizations were automatically segmented using a set of filtering and selection criteria (see Methods for details), leading to a total set of 10055 vocalizations. D We aimed to estimate the properties and the sex of its emitter for individual vocalizations. First, the ground truth for the properties were established by a human classifier. We next estimated 3 relations, Spectrogram-Properties, Properties-Sex and Spectrogram-Sex directly, using both a Deep Neural Network (DNN), support vector machines (SVM) and regularized linear regression (LR). E The properties attributed manually to individual vocalizations could take different values (rows, red number in each subpanel), illustrated here for a subset of the properties (columns). See Methods for a detailed list and description of the properties.