Acoustical deep learning methods for road pavement distress recognition

Author/s Alessandro Monticelli
Publishing Year 2024 Issue 2023/2
Language Italian Pages 7 P. 17-23 File size 0 KB
DOI 10.3280/ria2-2023oa15509
DOI is like a bar code for intellectual property: to have more infomation click here

FrancoAngeli is member of Publishers International Linking Association, Inc (PILA), a not-for-profit association which run the CrossRef service enabling links to and from online scholarly content.

In the following work, a deep learning-based methodology was proposed to evaluate road surface conditions starting from acoustic signals measured inside the car tire cavity.The project was carried out in collaboration with Ipool srl., in the context of the SURFAce project, funded by the Tuscany region. Three different classification architectures were proposed: An LSTM, based on the time series of a set of spectral descriptors, and two CNNs, the first focused on the signals’ spectrograms and the second on their Mel-requency cepstral coefficients (MFCCs). The ground truth data set was acquired through a mobile laboratory and classified through aptly developed analysis tools. Two of the three proposed architectures have provided encouraging results, and their implementation on portable systems could lead to real-time pavement classification in a cost and timeefficient way.

Keywords: ; tire cavity noise, deep learning, road condition assessment

  1. G. Bitelli, A. Simone, F. Girardi, C. Lantieri, Laser scanning on road pavements: A new approach for characterizing surface texture, Sensors 12 (2012) 9110-9128.
  2. M.A. Pallas, M. Bérengier, R. Chatagnon, M. Czuka, M. Conter, M. Muirhead, Towards a model for electric vehicle noise emission in
  3. the European prediction method CNOSSOS-EU, Appl. Acoust. 113 (2016) 89-101. 2016.06.012.
  4. ISO 11819-2:2017 Acoustics - Measurement of the influence of road surfaces on traffic noise - Part 2: The close-proximity method, International Organization for Standardization, Geneva, Switzerland, 2017.
  5. J. Masino, B. Daubner, M. Frey, F. Gauterin, Development of a tire cavity sound measurement system for the application of field operational tests, in: 10th Annual International Systems Conference, SysCon 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., Orlando, FL, 2016: 7490624.
  6. L.G. Del Pizzo, F. Bianco, A. Moro, G. Schiaffino, G. Licitra, Relationship between tyre cavity noise and road surface characteristics on low-noise pavements, Transport. Res. D-Tr. E. 98 (2021) 102971.
  7. ISO 13473-3:2002 Acoustics - Characterization of pavement texture by use of surface profiles, International Organization for Standardization, Geneva, Switzerland, 2002.
  8. P. Klein, J.F. Hamet, Road texture and rolling noise: an envelopment procedure for tire-road contact, 2004, 17p. hal00546120.
  9. A. Del Pizzo, Analysis of Tyre Rolling Noise on Low Noise Pavements, PhD Thesis, University of Pisa, Italy, 2021.
  10. J. Pinay, H. J. Unrau, F. Gauterin, Prediction of close-proximity tire-road noise from tire cavity noise measurements using a
  11. statistical approach, Appl. Acoust. 141 (2018) 293-300.
  12. G. Schiaffino, L. G. Del Pizzo, S. Silvestri, F. Bianco, G. Licitra, F.G. Pratico, Machine learning techniques applied to road health
  13. status recognition through tyre cavity noise analysis, J. Phys. Conf. Ser. 2162 (2022) 012011.
  14. Bollettino ufficiale della regione Lombardia - 1o supplemento straordinario. Allegato B, D.g.r. 25 gennaio 2006 - n. 8/1790 (in
  15. Italian).
  16. S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comp. 9 (1997) 1735-1780. 10.1162/neco.1997.9.8.1735.
  17. Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, A survey of convolutional neural networks: analysis, applications, and prospects, IEEE T. Neur. Net. Lear. 33 (2021) 6999-7019.
  18. K. Fukushima, S. Miyake, Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition, in: Amari, Si., Arbib, M.A. (eds) Competition and Cooperation in Neural Nets. Lecture Notes in Biomathematics, vol 45. Springer,
  19. Berlin, Heidelberg: pp. 267-285.
  20. S. Davis, P. Mermelstein, Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, IEEE T. Acoust. Speech. 28 (1980) 357-366.
  21. G. Peeters, A large set of audio features for sound description (similarity and classification) in the CUIDADO project, CUIDADO Project Report, 2004.
  22. A. Lerch, An introduction to audio content analysis: Applications in signal processing and music informatics, Wiley-IEEE Press, Hoboken, NJ, 2012.
  23. E. Scheirer, M. Slaney, Construction and evaluation of a robust multifeature speech/music discriminator, in: Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5), Institute of Electrical and Electronics Engineers, 1997: pp. 1331-1334.
  24. D.S. Wilks, Statistical methods in the atmospheric sciences, 3rd ed., Academic Press, Oxford, UK; Amsterdam, The Netherlands; Waltham, MA; San Diego, CA, 2011.
  25. S. Raschka, Python machine learning, 2nd ed., Packt Publishing Ltd, Birmingham, UK, 2015.
  26. Y. Wang, S. Ji, H. Xu, Non-stationary signals processing based on STFT, in: 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI, Xi’an, China, 2007: pp. 3301-3304.
  27. J. Pomerat, A. Segev, R. Datta, On neural network activation functions and optimizers in relation to polynomial regression, in: Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., Los Angeles, CA, 2019: pp. 6183-6185.
  28. Z. Zhang, Improved Adam optimizer for deep neural networks, in: 2018 IEEE/ACM 26th International Symposium on Quality of Service, IWQoS 2018, Institute of Electrical and Electronics Engineers Inc., Banff, Canada, 2018: pp. 1-2.

Alessandro Monticelli, Metodi di deep learning acustico per il riconoscimento dei dissesti della pavimentazione stradale in "RIVISTA ITALIANA DI ACUSTICA" 2/2023, pp 17-23, DOI: 10.3280/ria2-2023oa15509