Acoustic Classification of Cat Breed Based on Time and Frequency Domain Features

The emerging field of Bioacoustics has been presenting significant research activity lately, and thanks to the use of machine learning methods, several tools and methodologies have been established for identifying certain patterns and meanings in animal vocalizations. Animal sounds can vary over tim...

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Autores principales:	William Raccagni, Stavros Ntalampiras
Formato:	article
Lenguaje:	EN
Publicado:	FRUCT 2021
Materias:	audio classification cat breed bioacoustics animal vocalizations cat vocalizations Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/5c42e9fd449440309ea27d40453d0d35
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id	oai:doaj.org-article:5c42e9fd449440309ea27d40453d0d35
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spelling	oai:doaj.org-article:5c42e9fd449440309ea27d40453d0d352021-11-20T15:59:33ZAcoustic Classification of Cat Breed Based on Time and Frequency Domain Features2305-72542343-073710.23919/FRUCT53335.2021.9599975https://doaj.org/article/5c42e9fd449440309ea27d40453d0d352021-10-01T00:00:00Zhttps://www.fruct.org/publications/fruct30/files/Rac.pdfhttps://doaj.org/toc/2305-7254https://doaj.org/toc/2343-0737The emerging field of Bioacoustics has been presenting significant research activity lately, and thanks to the use of machine learning methods, several tools and methodologies have been established for identifying certain patterns and meanings in animal vocalizations. Animal sounds can vary over time in intensity and patterns produced between different breeds of the same species, both for physiological reasons and for different emotional states and needs. Pets, such as dogs and cats, are no exception, thus allowing a vocal distinction between breeds. This article studies classification of the cat breed, in particular on the Maine Coon and European Shorthair breed, based on the public audio dataset CatMeows. To this end, we employed features coming from time and frequency domain capturing relevant information as regard to the present audio structure. Subsequently, audio pattern recognition was carried out by means of k-means clustering, k-NN, and multilayer perceptron learning models. After extensive experiments, we obtained very promising results , with an average accuracy that runs around 98%. In particular, time-domain features presented a strong contribution, as demonstrated by the results using k-means.William RaccagniStavros NtalampirasFRUCTarticleaudio classificationcat breedbioacousticsanimal vocalizationscat vocalizationsTelecommunicationTK5101-6720ENProceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 30, Iss 1, Pp 184-189 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	audio classification cat breed bioacoustics animal vocalizations cat vocalizations Telecommunication TK5101-6720
spellingShingle	audio classification cat breed bioacoustics animal vocalizations cat vocalizations Telecommunication TK5101-6720 William Raccagni Stavros Ntalampiras Acoustic Classification of Cat Breed Based on Time and Frequency Domain Features
description	The emerging field of Bioacoustics has been presenting significant research activity lately, and thanks to the use of machine learning methods, several tools and methodologies have been established for identifying certain patterns and meanings in animal vocalizations. Animal sounds can vary over time in intensity and patterns produced between different breeds of the same species, both for physiological reasons and for different emotional states and needs. Pets, such as dogs and cats, are no exception, thus allowing a vocal distinction between breeds. This article studies classification of the cat breed, in particular on the Maine Coon and European Shorthair breed, based on the public audio dataset CatMeows. To this end, we employed features coming from time and frequency domain capturing relevant information as regard to the present audio structure. Subsequently, audio pattern recognition was carried out by means of k-means clustering, k-NN, and multilayer perceptron learning models. After extensive experiments, we obtained very promising results , with an average accuracy that runs around 98%. In particular, time-domain features presented a strong contribution, as demonstrated by the results using k-means.
format	article
author	William Raccagni Stavros Ntalampiras
author_facet	William Raccagni Stavros Ntalampiras
author_sort	William Raccagni
title	Acoustic Classification of Cat Breed Based on Time and Frequency Domain Features
title_short	Acoustic Classification of Cat Breed Based on Time and Frequency Domain Features
title_full	Acoustic Classification of Cat Breed Based on Time and Frequency Domain Features
title_fullStr	Acoustic Classification of Cat Breed Based on Time and Frequency Domain Features
title_full_unstemmed	Acoustic Classification of Cat Breed Based on Time and Frequency Domain Features
title_sort	acoustic classification of cat breed based on time and frequency domain features
publisher	FRUCT
publishDate	2021
url	https://doaj.org/article/5c42e9fd449440309ea27d40453d0d35
work_keys_str_mv	AT williamraccagni acousticclassificationofcatbreedbasedontimeandfrequencydomainfeatures AT stavrosntalampiras acousticclassificationofcatbreedbasedontimeandfrequencydomainfeatures
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Acoustic Classification of Cat Breed Based on Time and Frequency Domain Features

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