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Verfasst von:Herdt, Rudolf [VerfasserIn]   i
 Kinzel, Louisa [VerfasserIn]   i
 Maaß, Johann Georg [VerfasserIn]   i
 Walther, Marvin [VerfasserIn]   i
 Fröhlich, Henning [VerfasserIn]   i
 Schubert, Tim Felix [VerfasserIn]   i
 Maass, Peter [VerfasserIn]   i
 Schaaf, Christian P. [VerfasserIn]   i
Titel:Enhancing the analysis of murine neonatal ultrasonic vocalizations
Titelzusatz:development, evaluation, and application of different mathematical models
Verf.angabe:Rudolf Herdt, Louisa Kinzel, Johann Georg Maaß, Marvin Walther, Henning Fröhlich, Tim Schubert, Peter Maass and Christian Patrick Schaaf
E-Jahr:2024
Jahr:October 14 2024
Umfang:19 S.
Illustrationen:Illustrationen
Fussnoten:Gesehen am 24.03.2025
Titel Quelle:Enthalten in: Acoustical Society of AmericaThe journal of the Acoustical Society of America
Ort Quelle:Melville, NY : AIP Publ., 1929
Jahr Quelle:2024
Band/Heft Quelle:156(2024), 4 vom: Okt., Seite 2448-2466
ISSN Quelle:1520-8524
Abstract:Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactions, and developmental stages of animals, various deep learning approaches have aimed at automating both the quantitative (detection) and qualitative (classification) analysis of USVs. So far, no notable efforts have been made to determine the most suitable architecture. We present the first systematic evaluation of different types of neural networks for USV classification. We assessed various feedforward networks, including a custom-built, fully-connected network, a custom-built convolutional neural network, several residual neural networks, an EfficientNet, and a Vision Transformer. Our analysis concluded that convolutional networks with residual connections specifically adapted to USV data, are the most suitable architecture for analyzing USVs. Paired with a refined, entropy-based detection algorithm (achieving recall of 94.9 % and precision of 99.3 %), the best architecture (achieving 86.79 % accuracy) was integrated into a fully automated pipeline capable of analyzing extensive USV datasets with high reliability. In ongoing projects, our pipeline has proven to be a valuable tool in studying neonatal USVs. By comparing these distinct deep learning architectures side by side, we have established a solid foundation for future research.
DOI:doi:10.1121/10.0030473
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1121/10.0030473
 kostenfrei: Volltext: https://pubs.aip.org/asa/jasa/article/156/4/2448/3316833/enhancing-the-analysis-of-murine-neonatal
 DOI: https://doi.org/10.1121/10.0030473
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:192038443X
Verknüpfungen:→ Zeitschrift

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