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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  
dc.subject
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