Work

Analysis of ultrasonic vocalizations from mice using computer vision and machine learning

Year: 2021

Type: article

Abstract: Mice emit ultrasonic vocalizations (USVs) that communicate socially relevant information. To detect and classify these USVs, here we describe VocalMat. VocalMat is a software that uses image-processing and differential geometry approaches to detect USVs in audio files, eliminating the need for user-defined parameters. VocalMat also uses computational vision and machine learning methods to classify USVs into distinct categories. In a data set of >4000 USVs emitted by mice, VocalMat detected over 98% of manually labeled USVs and accurately classified ≈86% of the USVs out of 11 USV categories. We then used dimensionality reduction tools to analyze the probability distribution of USV classification among different experimental groups, providing a robust method to quantify and qualify the vocal repertoire of mice. Thus, VocalMat makes it possible to perform automated, accurate, and quantitative analysis of USVs without the need for user inputs, opening the opportunity for detailed and high-throughput analysis of this behavior.

Source: eLife

DOI: 10.7554/elife.59161

Authors: Antonio HO Fonseca, Gustavo M Santana, Gabriela M Bosque Ortiz, Sérgio Bampi, Marcelo O Dietrich

Institutions: Universidade Federal do Rio Grande do Sul, Yale University

Language: English

Topic: Animal Vocal Communication and Behavior

Subfield: Developmental Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,500