Abstract
Speeding is an important factor influencing road traffic safety. Even though speed is monitored using radars, the drivers may increase speed after passing the radar. In this paper, we address automatic classification of speed changes (or maintaining constant speed) from audio data, as a microphone added to the radar can register the drivers’ behavior both in front of and behind the radar. We propose two time-frequency based approaches to represent the audio data for speed classification purposes. These approaches have been tested in a pilot study using on-road data, and the results are presented in this paper.
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
References
Adobe. http://www.adobe.com/#
Ahn, I.-S., Bae, S.-G., Bae, M.-J.: Study on fault diagnosis of vehicles using the sound signal in audio signal processing. J. Eng. Technol. 3, 89–95 (2015)
Averbuch, A., Rabin, N., Schclar, A., Zheludev, V.: Dimensionality reduction for detection of moving vehicles. Pattern Anal. Appl. 15(1), 19–27 (2012)
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Duarte, M.F., Hu, Y.H.: Vehicle classification in distributed sensor networks. J. Parallel Distrib. Comput. 64, 826–838 (2004)
George, J., Cyril, A., Koshy, B.I., Mary, L.: Exploring sound signature for vehicle detection and classification using ANN. Int. J. Soft Comput. 4(2), 29–36 (2013)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Kubera, E., Wieczorkowska, A., Słowik, T., Kuranc, A., Skrzypiec, K.: Audio-based speed change classification for vehicles. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Raś, Z.W. (eds.) NFMCP 2016. LNAI, vol. 10312. Springer, Cham (to appear, 2017). http://www.springer.com/gp/book/9783319614601
Lyons, R.G.: Understanding Digital Signal Processing. Prentice Hall, Upper Saddle River (2012)
Sandberg, U.: The Multi-Coincidence Peak Around 1000 Hz in Tyre/Road Noise Spectra. Euro-Noise, Naples (2003)
Smith III., J.O., Serra, X.: PARSHL: an analysis/synthesis program for non-harmonic sounds based on a sinusoidal representation. In: International Computer Music Conference ICMC 1987. Computer Music Association (1987)
The R Foundation. http://www.R-project.org
Ziaran, S.: The assessment and evaluation of low-frequency noise near the region of infrasound. Noise Health 16(68), 10–17 (2014)
Acknowledgments
Work partially supported by the infrastructure bought within the project “Heterogenous Computation Cloud” funded by the Regional Operational Programme of Mazovia Voivodeship, and the Research Center of PJAIT, supported by the Ministry of Science and Higher Education in Poland.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wieczorkowska, A., Kubera, E., Koržinek, D., Słowik, T., Kuranc, A. (2017). Time-Frequency Representations for Speed Change Classification: A Pilot Study. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-60438-1_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60437-4
Online ISBN: 978-3-319-60438-1
eBook Packages: Computer ScienceComputer Science (R0)