Environmental Sound Recognition has become a relevant application for smart cities. Such an application, however, demands the use of trained machine learning classifiers in order to categorize a limited set of audio categories. Although classical machine learning solutions have been proposed in the past, most of the latest solutions that have been proposed toward automated and accurate sound classification are based on a deep learning approach. Deep learning models tend to be large, which can be problematic when considering that sound classifiers often have to be embedded in resource constrained devices. In this paper, a classical machine learning based classifier called MosAIc, and a lighter Convolutional Neural Network model for environmental sound recognition, are proposed to directly compete in terms of accuracy with the latest deep learning solutions. Both approaches are evaluated in an embedded system in order to identify the key parameters when placing such applications on constrained devices. The experimental results show that classical machine learning classifiers can be combined to achieve similar results to deep learning models, and even outperform them in accuracy. The cost, however, is a larger classification time.
Lhoest, L , Lamrini, M , Vandendriessche, J , Wouters, N , Silva, BD , Chkouri, MY & Touhafi, A 2021, ' MosAIc: A Classical Machine Learning Multi-Classifier Based Approach against Deep Learning Classifiers for Embedded Sound Classification ', Applied Sciences (Switzerland) , vol. 11, no. 18, 8394, pp. 1-27.
Lhoest, L. , Lamrini, M. , Vandendriessche, J. , Wouters, N. , Silva, B. D. , Chkouri, M. Y. , & Touhafi, A. (2021). MosAIc: A Classical Machine Learning Multi-Classifier Based Approach against Deep Learning Classifiers for Embedded Sound Classification . Applied Sciences (Switzerland) , 11 (18), 1-27. [8394].
@article{b880d6a254f44574bd16fb29f39abfd0,
title = " MosAIc: A Classical Machine Learning Multi-Classifier Based Approach against Deep Learning Classifiers for Embedded Sound Classification " ,
abstract = " Environmental Sound Recognition has become a relevant application for smart cities. Such an application, however, demands the use of trained machine learning classifiers in order to categorize a limited set of audio categories. Although classical machine learning solutions have been proposed in the past, most of the latest solutions that have been proposed toward automated and accurate sound classification are based on a deep learning approach. Deep learning models tend to be large, which can be problematic when considering that sound classifiers often have to be embedded in resource constrained devices. In this paper, a classical machine learning based classifier called MosAIc, and a lighter Convolutional Neural Network model for environmental sound recognition, are proposed to directly compete in terms of accuracy with the latest deep learning solutions. Both approaches are evaluated in an embedded system in order to identify the key parameters when placing such applications on constrained devices. The experimental results show that classical machine learning classifiers can be combined to achieve similar results to deep learning models, and even outperform them in accuracy. The cost, however, is a larger classification time. " ,
keywords = " Machine Learning, environment sound recognition, convolutional neural network, Embedded system, audio feature extraction, multi-class classification, one-vs-all " ,
author = " Lancelot Lhoest and Mimoun Lamrini and Jurgen Vandendriessche and Nick Wouters and Silva, {Bruno da} and Chkouri, {Mohamed Yassin} and Abdellah Touhafi " ,
note = " Funding Information: This work is part of the COllective Research NETworking (CORNET) project “AITIA: Embedded AI Techniques for Industrial Applications” [53]. The Belgian partners are funded by VLAIO under grant number HBC.2018.0491, while the German partners are funded by the BMWi (Federal Ministry for Economic Affairs and Energy) under IGF-Project Number 249 EBG. Publisher Copyright: { extcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. " ,
year = " 2021 " ,
month = sep,
day = " 10 " ,
doi = " 10.3390/app11188394 " ,
language = " English " ,
volume = " 11 " ,
pages = " 127 " ,
journal = " Applied Sciences " ,
issn = " 2076-3417 " ,
publisher = " MDPI " ,
number = " 18 " ,
}