An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks
Joint Authors
Celaya-Padilla, José
Martinez-Fierro, Margarita L.
Gamboa-Rosales, Hamurabi
Galván-Tejada, Carlos E.
Galván-Tejada, Jorge I.
Delgado-Contreras, J. Rubén
Magallanes-Quintanar, Rafael
Garza-Veloz, Idalia
López-Hernández, Yamilé
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-23
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Telecommunications Engineering
Abstract EN
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.
American Psychological Association (APA)
Galván-Tejada, Carlos E.& Galván-Tejada, Jorge I.& Celaya-Padilla, José& Delgado-Contreras, J. Rubén& Magallanes-Quintanar, Rafael& Martinez-Fierro, Margarita L.…[et al.]. 2016. An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks. Mobile Information Systems،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1111370
Modern Language Association (MLA)
Galván-Tejada, Carlos E.…[et al.]. An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks. Mobile Information Systems No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1111370
American Medical Association (AMA)
Galván-Tejada, Carlos E.& Galván-Tejada, Jorge I.& Celaya-Padilla, José& Delgado-Contreras, J. Rubén& Magallanes-Quintanar, Rafael& Martinez-Fierro, Margarita L.…[et al.]. An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks. Mobile Information Systems. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1111370
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111370