Acoustical deep learning methods for road pavement distress recognition

Journal title RIVISTA ITALIANA DI ACUSTICA
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
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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

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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