dc.contributor | Escuela de Ingenierias Industrial e Informatica | es_ES |
dc.contributor.author | García Ordás, María Teresa | |
dc.contributor.author | Alaiz Moretón, Héctor | |
dc.contributor.author | Benítez Andrades, José Alberto | |
dc.contributor.author | García-Rodríguez, Isaías | |
dc.contributor.author | García-Olalla, Oscar | |
dc.contributor.author | Benavides Cuéllar, María del Carmen | |
dc.contributor.other | Ingenieria de Sistemas y Automatica | es_ES |
dc.date | 2021-08 | |
dc.date.accessioned | 2022-03-10T12:53:33Z | |
dc.date.available | 2022-03-10T12:53:33Z | |
dc.identifier.citation | García-Ordás, M. T., Alaiz-Moretón, H., Benítez-Andrades, J. A., García-Rodríguez, I., García-Olalla, O., & Benavides, C. (2021). Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network. Biomedical Signal Processing and Control, 69. https://doi.org/10.1016/J.BSPC.2021.102946 | |
dc.identifier.issn | 1746-8094 | |
dc.identifier.other | https://www.sciencedirect.com/science/article/pii/S1746809421005437?via%3Dihub | es_ES |
dc.identifier.uri | http://hdl.handle.net/10612/14182 | |
dc.description.abstract | [EN] In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed. Mel spectrogram and Mel Frequency Cepstral Coefficients are used as audio description methods and a Fully Convolutional Neural Network architecture is proposed as a classifier. The results have been validated using three well known datasets: EMODB, RAVDESS and TESS. The results obtained were promising, outperforming the state-of–the-art methods. Also, thanks to the fact that the proposed method admits audios of any size, it allows a sentiment analysis to be made in near real time, which is very interesting for a wide range of fields such as call centers, medical consultations or financial brokers. | es_ES |
dc.language | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Ingeniería de sistemas | es_ES |
dc.subject.other | Sentiment analysis | es_ES |
dc.subject.other | Fully convolutional network | es_ES |
dc.subject.other | Real time | es_ES |
dc.subject.other | MFCC | es_ES |
dc.subject.other | Mel spectrograms | es_ES |
dc.title | Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.1016/j.bspc.2021.102946 | |
dc.description.peerreviewed | SI | es_ES |
dc.relation.projectID | info: eu-repo/grantAgreement/Ministerio de Economía y Empresa/TSI-100909–2019-64. | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.journal.title | Biomedical Signal Processing and Control | es_ES |
dc.volume.number | 69 | es_ES |
dc.page.initial | 102946 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |
dc.subject.unesco | 3304.17 Sistemas en Tiempo Real | es_ES |