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Title	Audio-Based Vehicle Detection Implementing Artificial Intelligence
ID_Doc	11080
Authors	Golovnin O.; Privalov A.; Stolbova A.; Ivaschenko A.
Year	2021
Published	Studies in Systems, Decision and Control, 337
DOI	http://dx.doi.org/10.1007/978-3-030-65283-8_51
Abstract	This paper presents a method for audio-based vehicle detection within the urban traffic flow analysis in Smart Cities. The proposed technology implements artificial neural networks to recognize and count vehicle sounds on audio recordings using mel-frequency cepstral coefficients. Nowadays there are a lot of different approaches for sound recognition but convolutional neural networks (CNN) have the greatest accuracy among the others. In this study, we compared CNN to a classic multilayer perceptron in the case of audio events recognition. The method was tested on the UrbanSound8K dataset and a mixed dataset combined by authors to be similar to actual conditions. Evaluation of possible intelligent solutions using the same UrbanSound8K dataset demonstrated that CNN have higher classification accuracy: 92.0% for CNN against 87.6% for multilayer perceptron. For the mixed dataset CNN presented the average vehicle detection accuracy of about 84.2%. Therefore, the proposed method allows simplification of traffic surveillance and reducing its costs and total information processing time. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	CNN; MFCC; Sound recognition; Traffic flow

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