Discriminative power of acoustic features for jaw movement classification in cattle and sheep

Autores
Galli, Julio Ricardo; Milone, Diego; Cangiano, Carlos Alberto; Martínez, César; Laca, Emilio; Chelotti, José; Rufiner, Leonardo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Measuring foraging behaviour and pasture intake of ruminants is useful but difficult. Acoustic monitoring is one of the most promising methods for this task. In this work, we test its potential to classify jaw movements (JM) according to type (bite, chew, chew-bite), activity (grazing or rumination), and forage species being consumed. Experiments with cows and sheep grazing and ruminating several forages were conducted. First, each JM was manually identified, classified and described by two sets of sound features: i) one containing energy bands of the spectrum (⁢), and ii) one containing four complementary (or global) variables (). Two models were evaluated, one with ⁢ alone and the other one combining ⁢ and . Jaw movements were correctly classified by type with 73.0% and 78.5% average accuracy. Accuracy was better for cows than sheep (85 vs 66%). Simultaneous identification of the type of JM and plant species was about 78%. Classification accuracy of activity based on chews averaged 68.5% and 77.0% for rumination and grazing. Models including global variables performed better than using only the spectrum. Acoustic monitoring is a very promising method for further development, particularly to study diet selection.
EEA Balcarce
Fil: Galli, Julio Ricardo. Universidad Nacional de Rosario (UNR). Facultad de Ciencias Agrarias; Argentina
Fil: Milone, Diego. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Milone, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Cangiano, Carlos Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Martínez, César. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Martínez, César. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Martínez, César. Universidad Nacional de Entre Ríos (UNER). Facultad de Ingeniería; Argentina
Fil: Laca, Emilio. University of California. Department of Plant Sciences; Estados Unidos
Fil: Chelotti, José. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Chelotti, José. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Rufiner, Leonardo. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Rufiner, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Rufiner, Leonardo. Universidad Nacional de Entre Ríos (UNER). Facultad de Ingeniería; Argentina
Fuente
Bioacoustics 29 (5) : 602-616 (2020)
Materia
Rumiante
Ganado Bovino
Ovinos
Mandíbula
Pastoreo
Ruminants
Cattle
Sheep
Mandible
Grazing
Acoustic Variables
Jaw Movements
Grazing Behaviour
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/28184
Acceder
id INTADig_2a298f8748047e2f5777ca1638a993e1
oai_identifier_str oai:localhost:20.500.12123/28184
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Discriminative power of acoustic features for jaw movement classification in cattle and sheepGalli, Julio RicardoMilone, DiegoCangiano, Carlos AlbertoMartínez, CésarLaca, EmilioChelotti, JoséRufiner, LeonardoRumianteGanado BovinoOvinosMandíbulaPastoreoRuminantsCattleSheepMandibleGrazingAcoustic VariablesJaw MovementsGrazing BehaviourMeasuring foraging behaviour and pasture intake of ruminants is useful but difficult. Acoustic monitoring is one of the most promising methods for this task. In this work, we test its potential to classify jaw movements (JM) according to type (bite, chew, chew-bite), activity (grazing or rumination), and forage species being consumed. Experiments with cows and sheep grazing and ruminating several forages were conducted. First, each JM was manually identified, classified and described by two sets of sound features: i) one containing energy bands of the spectrum (⁢), and ii) one containing four complementary (or global) variables (). Two models were evaluated, one with ⁢ alone and the other one combining ⁢ and . Jaw movements were correctly classified by type with 73.0% and 78.5% average accuracy. Accuracy was better for cows than sheep (85 vs 66%). Simultaneous identification of the type of JM and plant species was about 78%. Classification accuracy of activity based on chews averaged 68.5% and 77.0% for rumination and grazing. Models including global variables performed better than using only the spectrum. Acoustic monitoring is a very promising method for further development, particularly to study diet selection.EEA BalcarceFil: Galli, Julio Ricardo. Universidad Nacional de Rosario (UNR). Facultad de Ciencias Agrarias; ArgentinaFil: Milone, Diego. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Cangiano, Carlos Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Martínez, César. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Martínez, César. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Martínez, César. Universidad Nacional de Entre Ríos (UNER). Facultad de Ingeniería; ArgentinaFil: Laca, Emilio. University of California. Department of Plant Sciences; Estados UnidosFil: Chelotti, José. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Chelotti, José. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Leonardo. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Leonardo. Universidad Nacional de Entre Ríos (UNER). Facultad de Ingeniería; ArgentinaTaylor and Francis2026-10-20T13:08:56Z2026-10-20T13:08:56Z2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/28184https://www.tandfonline.com/doi/full/10.1080/09524622.2019.16339590952-4622https://doi.org/10.1080/09524622.2019.1633959Bioacoustics 29 (5) : 602-616 (2020)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-11-05T11:51:41Zoai:localhost:20.500.12123/28184instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-11-05 11:51:41.696INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Discriminative power of acoustic features for jaw movement classification in cattle and sheep
title Discriminative power of acoustic features for jaw movement classification in cattle and sheep
spellingShingle Discriminative power of acoustic features for jaw movement classification in cattle and sheep
Galli, Julio Ricardo
Rumiante
Ganado Bovino
Ovinos
Mandíbula
Pastoreo
Ruminants
Cattle
Sheep
Mandible
Grazing
Acoustic Variables
Jaw Movements
Grazing Behaviour
title_short Discriminative power of acoustic features for jaw movement classification in cattle and sheep
title_full Discriminative power of acoustic features for jaw movement classification in cattle and sheep
title_fullStr Discriminative power of acoustic features for jaw movement classification in cattle and sheep
title_full_unstemmed Discriminative power of acoustic features for jaw movement classification in cattle and sheep
title_sort Discriminative power of acoustic features for jaw movement classification in cattle and sheep
dc.creator.none.fl_str_mv Galli, Julio Ricardo
Milone, Diego
Cangiano, Carlos Alberto
Martínez, César
Laca, Emilio
Chelotti, José
Rufiner, Leonardo
author Galli, Julio Ricardo
author_facet Galli, Julio Ricardo
Milone, Diego
Cangiano, Carlos Alberto
Martínez, César
Laca, Emilio
Chelotti, José
Rufiner, Leonardo
author_role author
author2 Milone, Diego
Cangiano, Carlos Alberto
Martínez, César
Laca, Emilio
Chelotti, José
Rufiner, Leonardo
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Rumiante
Ganado Bovino
Ovinos
Mandíbula
Pastoreo
Ruminants
Cattle
Sheep
Mandible
Grazing
Acoustic Variables
Jaw Movements
Grazing Behaviour
topic Rumiante
Ganado Bovino
Ovinos
Mandíbula
Pastoreo
Ruminants
Cattle
Sheep
Mandible
Grazing
Acoustic Variables
Jaw Movements
Grazing Behaviour
dc.description.none.fl_txt_mv Measuring foraging behaviour and pasture intake of ruminants is useful but difficult. Acoustic monitoring is one of the most promising methods for this task. In this work, we test its potential to classify jaw movements (JM) according to type (bite, chew, chew-bite), activity (grazing or rumination), and forage species being consumed. Experiments with cows and sheep grazing and ruminating several forages were conducted. First, each JM was manually identified, classified and described by two sets of sound features: i) one containing energy bands of the spectrum (⁢), and ii) one containing four complementary (or global) variables (). Two models were evaluated, one with ⁢ alone and the other one combining ⁢ and . Jaw movements were correctly classified by type with 73.0% and 78.5% average accuracy. Accuracy was better for cows than sheep (85 vs 66%). Simultaneous identification of the type of JM and plant species was about 78%. Classification accuracy of activity based on chews averaged 68.5% and 77.0% for rumination and grazing. Models including global variables performed better than using only the spectrum. Acoustic monitoring is a very promising method for further development, particularly to study diet selection.
EEA Balcarce
Fil: Galli, Julio Ricardo. Universidad Nacional de Rosario (UNR). Facultad de Ciencias Agrarias; Argentina
Fil: Milone, Diego. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Milone, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Cangiano, Carlos Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Martínez, César. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Martínez, César. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Martínez, César. Universidad Nacional de Entre Ríos (UNER). Facultad de Ingeniería; Argentina
Fil: Laca, Emilio. University of California. Department of Plant Sciences; Estados Unidos
Fil: Chelotti, José. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Chelotti, José. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Rufiner, Leonardo. Universidad Nacional del Litoral (UNL). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Rufiner, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Rufiner, Leonardo. Universidad Nacional de Entre Ríos (UNER). Facultad de Ingeniería; Argentina
description Measuring foraging behaviour and pasture intake of ruminants is useful but difficult. Acoustic monitoring is one of the most promising methods for this task. In this work, we test its potential to classify jaw movements (JM) according to type (bite, chew, chew-bite), activity (grazing or rumination), and forage species being consumed. Experiments with cows and sheep grazing and ruminating several forages were conducted. First, each JM was manually identified, classified and described by two sets of sound features: i) one containing energy bands of the spectrum (⁢), and ii) one containing four complementary (or global) variables (). Two models were evaluated, one with ⁢ alone and the other one combining ⁢ and . Jaw movements were correctly classified by type with 73.0% and 78.5% average accuracy. Accuracy was better for cows than sheep (85 vs 66%). Simultaneous identification of the type of JM and plant species was about 78%. Classification accuracy of activity based on chews averaged 68.5% and 77.0% for rumination and grazing. Models including global variables performed better than using only the spectrum. Acoustic monitoring is a very promising method for further development, particularly to study diet selection.
publishDate 2020
dc.date.none.fl_str_mv 2020
2026-10-20T13:08:56Z
2026-10-20T13:08:56Z
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/20.500.12123/28184
https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1633959
0952-4622
https://doi.org/10.1080/09524622.2019.1633959
url http://hdl.handle.net/20.500.12123/28184
https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1633959
https://doi.org/10.1080/09524622.2019.1633959
identifier_str_mv 0952-4622
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Taylor and Francis
publisher.none.fl_str_mv Taylor and Francis
dc.source.none.fl_str_mv Bioacoustics 29 (5) : 602-616 (2020)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
_version_ 1881032529397415936
score 13.334379

Publicaciones similares