Computational method for segmentation and classification of ingestive sounds in sheep

Autores
Milone, Diego Humberto; Rufiner, Hugo Leonardo; Galli, Julio Ricardo; Laca, E.A.; Cangiano, Carlos Alberto
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work we propose a novel method to analyze and recognize automatically sound signals of chewing and biting. For the automatic segmentation and classification of acoustical ingestive behaviour of sheep the method use an appropriate acoustic representation and statistical modelling based on hidden Markov models. We analyzed 1813 seconds of chewing data from four sheep eating two different forages typically found in grazing production systems, orchardgrass and alfalfa, each at two sward heights. Because identification of species consumed when in mixed swards is a key issue in grazing science, we tested the possibility to discriminate species and sward height by using the proposed approach. Signals were correctly classified by forage and sward height in 67% of the cases, whereas forage was correctly identified 84% of the time. The results showed an overall performance of 82% for the recognition of chewing events.
Fil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina
Fil: Rufiner, Hugo Leonardo. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina
Fil: Galli, Julio Ricardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina
Fil: Laca, E.A.. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentina
Fil: Cangiano, Carlos Alberto. University of California; Estados Unidos
Materia
ACOUSTIC MODELING
HIDDEN MARKOV MODELS
GRAZING SHEEP
INGESTIVE 15 BEHAVIOUR
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/97578
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Computational method for segmentation and classification of ingestive sounds in sheepMilone, Diego HumbertoRufiner, Hugo LeonardoGalli, Julio RicardoLaca, E.A.Cangiano, Carlos AlbertoACOUSTIC MODELINGHIDDEN MARKOV MODELSGRAZING SHEEPINGESTIVE 15 BEHAVIOURhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this work we propose a novel method to analyze and recognize automatically sound signals of chewing and biting. For the automatic segmentation and classification of acoustical ingestive behaviour of sheep the method use an appropriate acoustic representation and statistical modelling based on hidden Markov models. We analyzed 1813 seconds of chewing data from four sheep eating two different forages typically found in grazing production systems, orchardgrass and alfalfa, each at two sward heights. Because identification of species consumed when in mixed swards is a key issue in grazing science, we tested the possibility to discriminate species and sward height by using the proposed approach. Signals were correctly classified by forage and sward height in 67% of the cases, whereas forage was correctly identified 84% of the time. The results showed an overall performance of 82% for the recognition of chewing events.Fil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Galli, Julio Ricardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; ArgentinaFil: Laca, E.A.. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; ArgentinaFil: Cangiano, Carlos Alberto. University of California; Estados UnidosElsevier2009-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/97578Milone, Diego Humberto; Rufiner, Hugo Leonardo; Galli, Julio Ricardo; Laca, E.A.; Cangiano, Carlos Alberto; Computational method for segmentation and classification of ingestive sounds in sheep; Elsevier; Computers and Eletronics in Agriculture; 65; 2; 3-2009; 228-2370168-1699CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0168169908002214info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2008.10.004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:59:37Zoai:ri.conicet.gov.ar:11336/97578instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:59:38.105CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Computational method for segmentation and classification of ingestive sounds in sheep
title Computational method for segmentation and classification of ingestive sounds in sheep
spellingShingle Computational method for segmentation and classification of ingestive sounds in sheep
Milone, Diego Humberto
ACOUSTIC MODELING
HIDDEN MARKOV MODELS
GRAZING SHEEP
INGESTIVE 15 BEHAVIOUR
title_short Computational method for segmentation and classification of ingestive sounds in sheep
title_full Computational method for segmentation and classification of ingestive sounds in sheep
title_fullStr Computational method for segmentation and classification of ingestive sounds in sheep
title_full_unstemmed Computational method for segmentation and classification of ingestive sounds in sheep
title_sort Computational method for segmentation and classification of ingestive sounds in sheep
dc.creator.none.fl_str_mv Milone, Diego Humberto
Rufiner, Hugo Leonardo
Galli, Julio Ricardo
Laca, E.A.
Cangiano, Carlos Alberto
author Milone, Diego Humberto
author_facet Milone, Diego Humberto
Rufiner, Hugo Leonardo
Galli, Julio Ricardo
Laca, E.A.
Cangiano, Carlos Alberto
author_role author
author2 Rufiner, Hugo Leonardo
Galli, Julio Ricardo
Laca, E.A.
Cangiano, Carlos Alberto
author2_role author
author
author
author
dc.subject.none.fl_str_mv ACOUSTIC MODELING
HIDDEN MARKOV MODELS
GRAZING SHEEP
INGESTIVE 15 BEHAVIOUR
topic ACOUSTIC MODELING
HIDDEN MARKOV MODELS
GRAZING SHEEP
INGESTIVE 15 BEHAVIOUR
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this work we propose a novel method to analyze and recognize automatically sound signals of chewing and biting. For the automatic segmentation and classification of acoustical ingestive behaviour of sheep the method use an appropriate acoustic representation and statistical modelling based on hidden Markov models. We analyzed 1813 seconds of chewing data from four sheep eating two different forages typically found in grazing production systems, orchardgrass and alfalfa, each at two sward heights. Because identification of species consumed when in mixed swards is a key issue in grazing science, we tested the possibility to discriminate species and sward height by using the proposed approach. Signals were correctly classified by forage and sward height in 67% of the cases, whereas forage was correctly identified 84% of the time. The results showed an overall performance of 82% for the recognition of chewing events.
Fil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina
Fil: Rufiner, Hugo Leonardo. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina
Fil: Galli, Julio Ricardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina
Fil: Laca, E.A.. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentina
Fil: Cangiano, Carlos Alberto. University of California; Estados Unidos
description In this work we propose a novel method to analyze and recognize automatically sound signals of chewing and biting. For the automatic segmentation and classification of acoustical ingestive behaviour of sheep the method use an appropriate acoustic representation and statistical modelling based on hidden Markov models. We analyzed 1813 seconds of chewing data from four sheep eating two different forages typically found in grazing production systems, orchardgrass and alfalfa, each at two sward heights. Because identification of species consumed when in mixed swards is a key issue in grazing science, we tested the possibility to discriminate species and sward height by using the proposed approach. Signals were correctly classified by forage and sward height in 67% of the cases, whereas forage was correctly identified 84% of the time. The results showed an overall performance of 82% for the recognition of chewing events.
publishDate 2009
dc.date.none.fl_str_mv 2009-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/97578
Milone, Diego Humberto; Rufiner, Hugo Leonardo; Galli, Julio Ricardo; Laca, E.A.; Cangiano, Carlos Alberto; Computational method for segmentation and classification of ingestive sounds in sheep; Elsevier; Computers and Eletronics in Agriculture; 65; 2; 3-2009; 228-237
0168-1699
CONICET Digital
CONICET
url http://hdl.handle.net/11336/97578
identifier_str_mv Milone, Diego Humberto; Rufiner, Hugo Leonardo; Galli, Julio Ricardo; Laca, E.A.; Cangiano, Carlos Alberto; Computational method for segmentation and classification of ingestive sounds in sheep; Elsevier; Computers and Eletronics in Agriculture; 65; 2; 3-2009; 228-237
0168-1699
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0168169908002214
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2008.10.004
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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