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Analysis of ultrasonic vocalizations from mice using computer vision and machine learning.
Antonio Ho Fonseca1,2, Gustavo M Santana1,2,3, Gabriela M Bosque Ortiz1,4
1Laboratory of Physiology of Behavior, Department of Comparative Medicine, Yale School of Medicine, New Haven, United States.
Elife
|March 31, 2021
Summary
VocalMat software automatically detects and classifies mouse ultrasonic vocalizations (USVs) with high accuracy. This tool enables detailed, high-throughput analysis of mouse vocal behavior without manual parameter adjustments.
Area of Science:
- Ethology
- Bioacoustics
- Computational Neuroscience
Background:
- Mice utilize ultrasonic vocalizations (USVs) for social communication.
- Accurate detection and classification of USVs are crucial for understanding mouse behavior.
- Existing methods often require manual parameter tuning, limiting throughput and objectivity.
Purpose of the Study:
- To introduce VocalMat, a novel software for automated detection and classification of mouse USVs.
- To provide a user-independent and high-throughput analysis tool for mouse vocalizations.
- To enable quantitative analysis of mouse vocal repertoires.
Main Methods:
- VocalMat employs image processing and differential geometry for USV detection.
- Computational vision and machine learning algorithms are used for USV classification.
- Dimensionality reduction tools analyze USV classification probabilities across experimental groups.
Main Results:
- VocalMat achieved >98% detection rate for manually labeled USVs in a dataset of over 4000 USVs.
- The software accurately classified approximately 86% of USVs into 11 distinct categories.
- A robust method for quantifying and qualifying mouse vocal repertoires was established.
Conclusions:
- VocalMat offers automated, accurate, and quantitative analysis of mouse USVs.
- The software eliminates the need for user-defined parameters, facilitating high-throughput studies.
- VocalMat opens new avenues for detailed behavioral analysis of mouse vocal communication.
