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.
Highlights
In animals, vocal communication transmits information about the state of the caller and influences the state of the listener
The segmented objects are further refined via morphological operations (Figure 1C and Figure 1 - Figure Supplement 1), resulting in a list of segmented blobs with their corresponding spectral features (Figure 1D)
Because experimental observations demonstrate a minimum of 10 ms of interval between two successive and distinct ultrasonic vocalizations (USV), we combined into a single USV
Summary
Vocal communication transmits information about the state of the caller and influences the state of the listener. Mice emit ultrasonic vocalizations (USVs) in a frequency range (≈30 – 110kHz) above the human hearing range (≈2 – 20kHz) (Zippelius and Schleidt, 1956; Noirot, 1972; Nyby et al, 1976, 1977b,a; Sales and Smith, 1978; Branchi et al, 2001; Hahn and Lavooy, 2005; Ehret, 2005; Branchi et al., 2006). These USVs are organized in phrases or bouts composed of sequences of syllables. Understanding the complex structure of USVs will advance vocal and social communications research
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