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Application of autoencoder to traffic noise analysis

Abstrakt

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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pobrano 102 razy
Wersja publikacji
Accepted albo Published Version
Licencja
Copyright (2019 Acoustical Society of America)

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Journal of the Acoustical Society of America nr 146, strony 2958 - 2958,
ISSN: 0001-4966
Język:
angielski
Rok wydania:
2019
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
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1121/1.5137275
Bibliografia: test
  1. X. V. Gonzalez and F. Alías, "Automatic classification of road vehicles considering their pass-by acoustic signature," Proc. Meet. Acoust., vol. 19, 2013.
  2. K. Marciniuk, B. Kostek, and A. Czyżewski, "Traffic Noise Analysis Applied to Automatic Vehicle Counting and Classification," 2017, pp. 110-123. otwiera się w nowej karcie
  3. M. A. Sobreira-Seoane, A. Rodríguez Molares, and J. L. Alba Castro, "Automatic classification of traffic noise," Proc. -Eur. Conf. Noise Control, no. June, pp. 6221-6226, 2008. otwiera się w nowej karcie
  4. M. A. D, B. S. A, and M. M. H, "Classification of Vehicles Based on Audio Signals using Quadratic Discriminant Analysis and High Energy Feature Vectors," Int. J. Soft Comput., vol. 6, no. 1, pp. 53-64, 2015.
  5. M. Freitag, S. Amiriparian, S. Pugachevskiy, N. Cummins, and B. Schuller, "auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks," J. Mach. Learn. Res., vol. 18, 2017.
  6. J. Chorowski, R. Weiss, S. Bengio, and A. Oord, "Unsupervised Speech Representation Learning Using WaveNet Autoencoders," IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. PP, p. 1, 2019. otwiera się w nowej karcie
  7. F. Li et al., "Feature extraction and classification of heart sound using 1D convolutional neural networks," EURASIP J. Adv. Signal Process., vol. 2019, p. 59, 2019. otwiera się w nowej karcie
  8. J. Kotus and G. Szwoch, "Calibration of acoustic vector sensor based on MEMS microphones for DOA estimation," Appl. Acoust., vol. 141, no. July, pp. 307-321, 2018. otwiera się w nowej karcie
  9. A. Kurowski, K. Marciniuk, and B. Kostek, "Separability Assessment of Selected Types of Vehicle- Associated Noise," in Multimedia and Network Information Systems, 2017, pp. 113-121. otwiera się w nowej karcie
  10. K. Marciniuk, M. Szczodrak, and A. Czyzewski, "An application of acoustic sensors for the monitoring of road traffic," 2018 Signal Process. Algorithms, Archit. Arrange. Appl., pp. 208-212, 2018. otwiera się w nowej karcie
  11. J. Kotus, "Determination of the Vehicles Speed Using Acoustic Vector Sensor," in 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2018, pp. 64-69. otwiera się w nowej karcie
  12. A. Kurowski, A. Czyżewski, and S. Zaporowski, "SPA 2019 Automatic labeling of traffic sound recordings using autoencoder-derived features," pp. 38-43, 2019. otwiera się w nowej karcie
  13. A. Czyżewski, "Free-standing intelligent road sign," 125160, 2019 Polish patent No. W.125160,.
  14. A. Czyzewski, "Hanging intelligent road sign," 125159, 2019. Polish patent No. W.125159
  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:
Weryfikacja:
Politechnika Gdańska

wyświetlono 94 razy

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