Please use this identifier to cite or link to this item:
http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1458
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor | 299983 | es_ES |
dc.contributor | 49237 | es_ES |
dc.contributor | 268446 | - |
dc.contributor.other | https://orcid.org/0000-0002-9498-6602 | - |
dc.contributor.other | 0000-0002-9498-6602 | - |
dc.coverage.spatial | Global | es_ES |
dc.creator | Galván Tejada, Carlos Eric | - |
dc.creator | Galván Tejada, Jorge | - |
dc.creator | Celaya Padilla, José María | - |
dc.creator | Delgado Contreras, Juan Rubén | - |
dc.creator | Magallanes Quintanar, Rafael | - |
dc.creator | Martínez Fierro, Margarita de la Luz | - |
dc.creator | Garza Veloz, Idalia | - |
dc.creator | López Hernández, Yamilé | - |
dc.creator | Gamboa Rosales, Hamurabi | - |
dc.date.accessioned | 2020-03-25T02:52:46Z | - |
dc.date.available | 2020-03-25T02:52:46Z | - |
dc.date.issued | 2016-11-23 | - |
dc.identifier | info:eu-repo/semantics/publishedVersion | es_ES |
dc.identifier.issn | 1875-905X | es_ES |
dc.identifier.uri | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1458 | - |
dc.description.abstract | This work presents a human activity recognition (HAR) model based on audio features. The use of sound as an information source for HAR models represents a challenge because sound wave analyses generate very large amounts of data. However, feature selection techniques may reduce the amount of data required to represent an audio signal sample. Some of the audio features that were analyzed include Mel-frequency cepstral coefficients (MFCC). Although MFCC are commonly used in voice and instrument recognition, their utility within HAR models is yet to be confirmed, and this work validates their usefulness. Additionally, statistical features were extracted from the audio samples to generate the proposed HAR model. The size of the information is necessary to conform a HAR model impact directly on the accuracy of the model. This problem also was tackled in the present work; our results indicate that we are capable of recognizing a human activity with an accuracy of 85% using the HAR model proposed. This means that minimum computational costs are needed, thus allowing portable devices to identify human activities using audio as an information source. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Hindawi | es_ES |
dc.relation | http://dx.doi.org/10.1155/2016/1784101 | es_ES |
dc.relation.uri | generalPublic | es_ES |
dc.rights | Atribución-NoComercial-CompartirIgual 3.0 Estados Unidos de América | * |
dc.rights | Atribución-NoComercial-CompartirIgual 3.0 Estados Unidos de América | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
dc.source | Hindawi Vol. 2016, pp. 1-10 | es_ES |
dc.subject.classification | INGENIERIA Y TECNOLOGIA [7] | es_ES |
dc.subject.other | Analysis of Audio | es_ES |
dc.subject.other | Neural Networks | es_ES |
dc.subject.other | Activity Recognition Model Using Genetic Algorithms | es_ES |
dc.title | An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
Appears in Collections: | *Documentos Académicos*-- Doc. en Ing. y Tec. Aplicada |
Files in This Item:
File | Description | Size | Format | |
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An Analysis of Audio Features.pdf | 2,51 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License