Application of autoencoder to traffic noise analysis - Publikacja - MOST Wiedzy


Application of autoencoder to traffic noise analysis


The aim of an autoencoder neural network is to transform the input data into a lower-dimensional code and then to reconstruct the output from this code representation. Applications of autoencoders to classifying sound events in the road traffic have not been found in the literature. The presented research aims to determine whether such an unsupervised learning method may be used for deploying classification algorithms applied to the automatic annotation of road traffic-related events based on noise analysis. Two-dimensional representation of traffic sounds based on Mel Frequency Cepstral Coefficients (MFCC) was fed the autoencoder neural network, and after that classified with k-nearest neighbors algorithm, Support Vector Machines, and random forests. Obtained results show that sound recordings can help determine the number of vehicles passing on the road. However, instead of being treated as independent, this method output should be combined with another source of data, e.g., video processing results or microwave radar data readings. Comparative results of vehicle counting obtained with the use of autoencoder and different classifiers are shown in the paper. [The Polish National Centre finances the project for Research and Development (NCBR) from the European Regional Development Fund No. POIR.04.01.04-00-0089/16 entitled: "INZNAK: Intelligent Road Signs with V2X Interface for Adaptive Traffic Controlling."]


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Copyright (2019 Acoustical Society of America)

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Opublikowano w:
Journal of the Acoustical Society of America nr 146, strony 2958 - 2958,
ISSN: 0001-4966
Rok wydania:
Opis bibliograficzny:
Czyżewski A., Kurowski A., Zaporowski S.: Application of autoencoder to traffic noise analysis// Journal of the Acoustical Society of America -Vol. 146,iss. 4 (2019), s.2958-2958
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1121/1.5137275
Bibliografia: test
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  15. N. Buduma and N. Locascio, Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, 1st ed. O'Reilly Media, Inc., 2017.
Źródła finansowania:
Politechnika Gdańska

wyświetlono 46 razy

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