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HybridMouse: A Hybrid Convolutional-Recurrent Neural Network-Based Model for Identification of Mouse Ultrasonic Vocalizations

Mice use ultrasonic vocalizations (USVs) to convey a variety of socially relevant information. These vocalizations are affected by the sex, age, strain, and emotional state of the emitter and can thus be used to characterize it. Current tools used to detect and analyze murine USVs rely on user input...

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Autores principales: Goussha, Yizhaq, Bar, Kfir, Netser, Shai, Cohen, Lior, Hel-Or, Yacov, Wagner, Shlomo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823244/
https://www.ncbi.nlm.nih.gov/pubmed/35145383
http://dx.doi.org/10.3389/fnbeh.2021.810590
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author Goussha, Yizhaq
Bar, Kfir
Netser, Shai
Cohen, Lior
Hel-Or, Yacov
Wagner, Shlomo
author_facet Goussha, Yizhaq
Bar, Kfir
Netser, Shai
Cohen, Lior
Hel-Or, Yacov
Wagner, Shlomo
author_sort Goussha, Yizhaq
collection PubMed
description Mice use ultrasonic vocalizations (USVs) to convey a variety of socially relevant information. These vocalizations are affected by the sex, age, strain, and emotional state of the emitter and can thus be used to characterize it. Current tools used to detect and analyze murine USVs rely on user input and image processing algorithms to identify USVs, therefore requiring ideal recording environments. More recent tools which utilize convolutional neural networks models to identify vocalization segments perform well above the latter but do not exploit the sequential structure of audio vocalizations. On the other hand, human voice recognition models were made explicitly for audio processing; they incorporate the advantages of CNN models in recurrent models that allow them to capture the sequential nature of the audio. Here we describe the HybridMouse software: an audio analysis tool that combines convolutional (CNN) and recurrent (RNN) neural networks for automatically identifying, labeling, and extracting recorded USVs. Following training on manually labeled audio files recorded in various experimental conditions, HybridMouse outperformed the most commonly used benchmark model utilizing deep-learning tools in accuracy and precision. Moreover, it does not require user input and produces reliable detection and analysis of USVs recorded under harsh experimental conditions. We suggest that HybrideMouse will enhance the analysis of murine USVs and facilitate their use in scientific research.
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spelling pubmed-88232442022-02-09 HybridMouse: A Hybrid Convolutional-Recurrent Neural Network-Based Model for Identification of Mouse Ultrasonic Vocalizations Goussha, Yizhaq Bar, Kfir Netser, Shai Cohen, Lior Hel-Or, Yacov Wagner, Shlomo Front Behav Neurosci Behavioral Neuroscience Mice use ultrasonic vocalizations (USVs) to convey a variety of socially relevant information. These vocalizations are affected by the sex, age, strain, and emotional state of the emitter and can thus be used to characterize it. Current tools used to detect and analyze murine USVs rely on user input and image processing algorithms to identify USVs, therefore requiring ideal recording environments. More recent tools which utilize convolutional neural networks models to identify vocalization segments perform well above the latter but do not exploit the sequential structure of audio vocalizations. On the other hand, human voice recognition models were made explicitly for audio processing; they incorporate the advantages of CNN models in recurrent models that allow them to capture the sequential nature of the audio. Here we describe the HybridMouse software: an audio analysis tool that combines convolutional (CNN) and recurrent (RNN) neural networks for automatically identifying, labeling, and extracting recorded USVs. Following training on manually labeled audio files recorded in various experimental conditions, HybridMouse outperformed the most commonly used benchmark model utilizing deep-learning tools in accuracy and precision. Moreover, it does not require user input and produces reliable detection and analysis of USVs recorded under harsh experimental conditions. We suggest that HybrideMouse will enhance the analysis of murine USVs and facilitate their use in scientific research. Frontiers Media S.A. 2022-01-25 /pmc/articles/PMC8823244/ /pubmed/35145383 http://dx.doi.org/10.3389/fnbeh.2021.810590 Text en Copyright © 2022 Goussha, Bar, Netser, Cohen, Hel-Or and Wagner. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Behavioral Neuroscience
Goussha, Yizhaq
Bar, Kfir
Netser, Shai
Cohen, Lior
Hel-Or, Yacov
Wagner, Shlomo
HybridMouse: A Hybrid Convolutional-Recurrent Neural Network-Based Model for Identification of Mouse Ultrasonic Vocalizations
title HybridMouse: A Hybrid Convolutional-Recurrent Neural Network-Based Model for Identification of Mouse Ultrasonic Vocalizations
title_full HybridMouse: A Hybrid Convolutional-Recurrent Neural Network-Based Model for Identification of Mouse Ultrasonic Vocalizations
title_fullStr HybridMouse: A Hybrid Convolutional-Recurrent Neural Network-Based Model for Identification of Mouse Ultrasonic Vocalizations
title_full_unstemmed HybridMouse: A Hybrid Convolutional-Recurrent Neural Network-Based Model for Identification of Mouse Ultrasonic Vocalizations
title_short HybridMouse: A Hybrid Convolutional-Recurrent Neural Network-Based Model for Identification of Mouse Ultrasonic Vocalizations
title_sort hybridmouse: a hybrid convolutional-recurrent neural network-based model for identification of mouse ultrasonic vocalizations
topic Behavioral Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823244/
https://www.ncbi.nlm.nih.gov/pubmed/35145383
http://dx.doi.org/10.3389/fnbeh.2021.810590
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