An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks
المؤلفون المشاركون
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
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-11-23
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1111370
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر