A Comparative Study on Denoising Algorithms for Footsteps Sounds as Biometric in Noisy Environments

Vista sencilla de ítem
dc.creatorCaravaca Mora, Ronald
dc.creatorBrenes Jiménez, Carlos
dc.creatorCoto Jiménez, Marvin
dc.date.accessioned2022-08-17T17:35:35Z
dc.date.available2022-08-17T17:35:35Z
dc.date.issued2022-08-03
dc.description.abstractBiometrics is the automated identification of a person based on distinctive characteristics, such as fingerprints, face, voice, or the sound of footsteps. This last characteristic has significant challenges considering the background noise present in any real-life application, where microphones would record footsteps sounds and different types of noise. For this reason, it is crucial to consider not only the capacity of classification algorithms for recognizing a person using foostetps sounds, but also at least one stage of denoising algorithms that can reduce the background sounds before the classification. In this paper we study the possibilities of a two-stage approach for this problem: a denoising stage followed by a classification process. The work focuses on discovering the proper strategy for applying combinations of both stages for specific noise types and levels. Results vary according to the type and level of noise, e.g., for White noise at signal-to-noise ratio level, accuracy can increase from 0.96 to 1.00 by applying deep learning based-filters, but the same option does not benefit the cases of signals with low level natural noises, where Wiener filtering can increase accuracy from 0.6 to 0.77 at the highest level of noise. The results represent a baseline for developing real-life implementations of footstep biometrics.es_ES
dc.description.procedenceUCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ingeniería Eléctricaes_ES
dc.description.sponsorshipUniversidad de Costa Rica/322–B9-105/UCR/Costa Ricaes_ES
dc.identifier.citationhttps://www.mdpi.com/2079-3197/10/8/133es_ES
dc.identifier.codproyecto322-B9-105
dc.identifier.doi10.3390/computation10080133
dc.identifier.issn2079-3197
dc.identifier.urihttps://hdl.handle.net/10669/87186
dc.language.isoenges_ES
dc.rightsacceso abierto
dc.sourceComputation; Vol. 10 Núm. 8: 2022es_ES
dc.subjectBIOMETRICSes_ES
dc.subjectCLASSIFICATION SYSTEMSes_ES
dc.subjectFilteringes_ES
dc.subjectFootstepses_ES
dc.subjectNOISEes_ES
dc.titleA Comparative Study on Denoising Algorithms for Footsteps Sounds as Biometric in Noisy Environmentses_ES
dc.typeartículo originales_ES

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
computation-10-00133.pdf
Tamaño:
1.78 MB
Formato:
Adobe Portable Document Format
Descripción:
Artículo principal
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.5 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Ingeniería eléctrica