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Open AccessArticle

Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognition on Embedded Systems

1
Department of Engineering Technology (INDI), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium
2
Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(18), 3885; https://doi.org/10.3390/app9183885
Received: 16 July 2019 / Revised: 10 September 2019 / Accepted: 11 September 2019 / Published: 16 September 2019
(This article belongs to the Special Issue Recent Advances on Wireless Acoustic Sensor Networks (WASN))
Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi-thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing. View Full-Text
Keywords: urban sound classification; machine learning; embedded system; environment sound recognition; audio feature extraction; edge computing urban sound classification; machine learning; embedded system; environment sound recognition; audio feature extraction; edge computing
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MDPI and ACS Style

da Silva, B.; W. Happi, A.; Braeken, A.; Touhafi, A. Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognition on Embedded Systems. Appl. Sci. 2019, 9, 3885.

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