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dc.contributor.author
Vignolo, Leandro Daniel
dc.contributor.author
Albornoz, Enrique Marcelo
dc.contributor.author
Martínez, César Ernesto
dc.date.available
2020-07-02T15:06:25Z
dc.date.issued
2019-07
dc.identifier.citation
Vignolo, Leandro Daniel; Albornoz, Enrique Marcelo; Martínez, César Ernesto; Exploring feature extraction methods for infant mood classification; IOS Press; AI Communications; 32; 3; 7-2019; 191-206
dc.identifier.issn
0921-7126
dc.identifier.uri
http://hdl.handle.net/11336/108647
dc.description.abstract
Speaker state recognition is an important issue to understand the human behaviour and to achieve more comprehensive speech interactive systems, and therefore has received much attention in recent years. This work addresses the automatic classification of three types of child emotions in vocalisations: neutral mood, fussing (negative mood) and crying (negative mood). Speech, in a broad sense, contains a lot of para-linguistic information that can be revealed by means of different methods for feature extraction and, in this case, these would be useful for mood detection. Here, several set of features are proposed, combined and compared with state-of-art characteristics used for speech-related tasks, and these are based on spectral information, bio-inspired ear model, auditory sparse representations with dictionaries, optimised wavelet coefficients and optimised filter bank for cepstral representation. All the experiments were performed using the Extreme Learning Machines as classifier because it is a state-of-art classifier and to achieve comparable results. The results show that by means of the proposed feature extraction methods it is possible to improve the performance provided by the baseline features. Also, different combinations of the developed feature sets were studied in order to further exploit their properties.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
IOS Press
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
BIO-INSPIRED EAR MODEL
dc.subject
CRYING DETECTION
dc.subject
FILTER BANK OPTIMISATION
dc.subject
MOOD CLASSIFICATION
dc.subject
SPARSE REPRESENTATIONS
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SPECTRAL FEATURES
dc.subject
WAVELET PACKETS
dc.subject.classification
Otras Ciencias de la Computación e Información
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Exploring feature extraction methods for infant mood classification
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-07-01T20:07:38Z
dc.journal.volume
32
dc.journal.number
3
dc.journal.pagination
191-206
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Vignolo, Leandro Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil
Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil
Fil: Martínez, César Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.journal.title
AI Communications
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/AIC-190620
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3233/AIC-190620
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