MosAIc: A Classical Machine Learning Multi-Classifier Based Approach against Deep Learning Classifiers for Embedded Sound Classification
| Autor: | Mimoun Lamrini, Nick Wouters, Jurgen Vandendriessche, Bruno da Silva, Mohamed Yassin Chkouri, Lancelot Charles Lhoest, Abdellah Touhafi |
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| Přispěvatelé: | Multidimensional signal processing and communication, Electronics and Informatics, Faculty of Engineering, Engineering Technology |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Computer science QH301-705.5 QC1-999 Mosaic (geodemography) Convolutional neural network Machine learning computer.software_genre Set (abstract data type) Multiclass classification Machine Learning embedded system Classifier (linguistics) audio feature extraction General Materials Science Biology (General) Instrumentation QD1-999 Fluid Flow and Transfer Processes business.industry Process Chemistry and Technology Deep learning Physics General Engineering Engineering (General). Civil engineering (General) multi-class classification Computer Science Applications Chemistry one-vs-all Categorization environment sound recognition Key (cryptography) Artificial intelligence TA1-2040 business computer |
| Zdroj: | Applied Sciences Volume 11 Issue 18 Applied Sciences, Vol 11, Iss 8394, p 8394 (2021) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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