Automatic classification of mice vocalizations using Machine Learning techniques and Convolutional Neural Networks.

ABSTRACT: Ultrasonic vocalizations (USVs) analysis is a well-recognized tool to investigate animal communication. It can be used for behavioral phenotyping of murine models of different disorders. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and they are analyzed by specific software. Different calls typologies exist, and each ultrasonic call can be manually classified, but the qualitative analysis is highly time-consuming. Considering this framework, in this work we proposed and evaluated a set of supervised learning methods for automatic USVs classification. This could represent a sustainable procedure to deeply analyze the ultrasonic communication, other than a standardized analysis. We used manually built datasets obtained by segmenting the USVs audio track

SUBMITTER: Premoli M 

PROVIDER: S-EPMC7815145 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature