Abstract
In this paper we address automatic vehicle identification based on audio information. Such data are complicated, as they depend on vehicle type, tires, speed and its change. In our previous research we designed a feature set for selected vehicle classes, discriminating pairs of classes. Now, we decided to expand the feature vector and find the best feature set (mainly based on spectral descriptors), possibly representative for each investigated vehicle category, which can be applied to a bigger data set, with more classes. The paper also shows problems related to vehicles classification, which is detailed in official documents by national authority for issues related to the national road system, but simplified for automatic identification purposes. Experiments on audio-based vehicle type identification are presented and conclusions are shown.
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This work was partially supported by the Research Center of PJAIT, supported by the Ministry of Science and Higher Education in Poland.
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Wieczorkowska, A., Kubera, E., Słowik, T., Skrzypiec, K. (2016). Spectral Features for Audio Based Vehicle Identification. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2015. Lecture Notes in Computer Science(), vol 9607. Springer, Cham. https://doi.org/10.1007/978-3-319-39315-5_11
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