Publication:
A Dataset and TinyML Model for Coarse Age Classification Based on Voice Commands

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dc.citedby2
dc.contributor.authorKadir A.D.I.A.en_US
dc.contributor.authorAl-Haiqi A.en_US
dc.contributor.authorDin N.M.en_US
dc.contributor.authorid57426768900en_US
dc.contributor.authorid57510682900en_US
dc.contributor.authorid9335429400en_US
dc.date.accessioned2023-05-29T09:09:58Z
dc.date.available2023-05-29T09:09:58Z
dc.date.issued2021
dc.descriptionClassification (of information); Data acquisition; Internet of things; Learning systems; Statistical tests; Age classification; Arduino nano BLE 33 sense; Data collection; Data preprocessing; Edge impulse; Machine learning models; Machine-learning; Sub-disciplines; Tiny machine learning; Voice command; Deep learningen_US
dc.description.abstractThis study explores the emerging sub-discipline of Tiny Machine Learning (TinyML) in the specific context of classifying audio data. New problems arise from the anticipation of voice prevalence in future interfaces with machines, especially embedded ones, and the need to have embedded intelligence into those machines, which can be captured by the idea of TinyML. In particular, there is a lack of studies on TinyML models for age classification based on voice commands, especially involving children as subjects. There is also a lack of datasets that include voice commands of both adults and children. This study aims to develop a TinyML model that is able to discriminate adults from children from their voice commands. The methodology follows the workflow of building deep learning models, including data collection and preprocessing, training, testing and deployment. Beyond the tools used for data collection and preprocessing, Edge Impulse was adopted as the development platform to train and test the model. The evaluation of the model showed a classification accuracy of more than 97% based on the custom dataset built to train and test the model. Finally, the tiny model was successfully deployed and evaluated on an Arduino Nano 33 BLE sense microcontroller. � 2021 IEEEen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/MICC53484.2021.9642091
dc.identifier.epage80
dc.identifier.scopus2-s2.0-85123943605
dc.identifier.spage75
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123943605&doi=10.1109%2fMICC53484.2021.9642091&partnerID=40&md5=6266300f8768436ae5407113a1015fed
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26398
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle15th IEEE Malaysia International Conference on Communications: Emerging Technologies in IoT and 5G, MICC 2021 - Proceedings
dc.titleA Dataset and TinyML Model for Coarse Age Classification Based on Voice Commandsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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