Abstract
In this study we address automatic vehicle and engine identification based on audio information. This information be based on many factors, such as vehicle type, tires, speed, wear and tear of vehicles, as well as type of road. We have decided a feature set for discriminating pairs of classes. Feature set include Fibonacci feature space, entropy, skewness and kurtosis. The audio information collected are real time on-road recordings. There are four classes of vehicle sounds. The paper also shows problems related to vehicles classification. Classification on audio-based engine and vehicle type identification are proposed and conclusions are shown.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Paulraj, M.P., et al.: Moving vehicle recognition and classification based on time domain approach. Procedia Eng. 53, 405–410 (2013)
Nooralahiyan, Y., et al.: Field trial of acoustic signature analysis for vehicle classification. Transp. Res. Part C: Emerg. Technol. 5, 165–177 (1997)
Wu, H., Siegel, M., Khosla, P.: “Vehicle sound signature recognition by frequency vector principal component analysis." In: IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference, vol. 1, pp. 429–434 (1998)
Maciejewski, H., Mazurkiewicz, J., Skowron, K., Walkowiak, T.: “Neural networks for vehicle recognition." In: Proceeding of the 6th International Conference on Microelectronics for Neural Networks, Evolutionary and Fuzzy Systems, vol. 1, p. 5 (1997)
Dalir, A., Beheshti, A.A., Masoom, M.H.: “Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors." arXiv preprint arXiv 1804.01212 (2018)
Lopez, J.E., Chen, H.H., Saulnier, J.: “Target identification using wavelet-based feature extraction and neural network classifiers." CYTEL SYSTEMS INC HUDSON MA (1999)
Ankishan, H.: Classification of acoustic signals with new feature: fibonacci space (FSp). Biomed. Signal Process. Control 48, 221–233 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Sinha, A., Kumar, S.H., Prabhakar, G.A., Rao, C.V.R. (2022). Extraction of Temporal Features on Fibonacci Space for Audio Based Vehicle Classification. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-07005-1_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-07004-4
Online ISBN: 978-3-031-07005-1
eBook Packages: Computer ScienceComputer Science (R0)