Machine learning based animal emotion classification using audio signals

Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/14589

Title:	Machine learning based animal emotion classification using audio signals
Authors:	Slobodian, Mariia Kozlenko, Mykola Козленко, Микола Іванович Слободян, Марія
Keywords:	acoustic features audio signals dog vocalization analysis machine learning deep learning artificial neural network mobile application cepstral coefficients sound segmentation
Issue Date:	29-Nov-2022
Publisher:	Vasyl Stefanyk Precarpathian National University
Citation:	M. Slobodian and M. Kozlenko, "Machine learning based animal emotion classification using audio signals," 2022 International Conference on Innovative Solutions in Software Engineering (ICISSE), Vasyl Stefanyk Precarpathian National University, Ivano-Frankivsk, Ukraine, Nov. 29-30, 2022, pp. 277-281, doi: 10.5281/zenodo.7514137
Abstract:	This paper presents the machine learning approach to the automated classification of a dog's emotional state based on the processing and recognition of audio signals. It offers helpful information for improving human-machine interfaces and developing more precise tools for classifying emotions from acoustic data. The presented model demonstrates an overall accuracy value above 70% for audio signals recorded for one dog.
URI:	https://zenodo.org/record/7514137 http://hdl.handle.net/123456789/14589
ISBN:	978-966-640-534-3
Appears in Collections:	Статті та тези (ФМІ)

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