ANALYSIS OF PORTABLE SYSTEM FOR SOUND ACQUISITION OF VEHICLES POWERED BY INTERNAL COMBUSTION ENGINES

Marko Milivojčević, Emilija Kisić, Dejan Ćirić

DOI Number
doi.org/10.2298/FUEE2302299M
First page
299
Last page
314

Abstract


In this paper a portable system for acquisition of sound generated by passenger vehicles powered by internal combustion engines is described and analyzed. The acquisition system is developed from scratch and tested in order to satisfy the requirements such as high-quality of audio recordings, high mobility, robustness and privacy respect. With this acquisition system and adequate signal processing, the main goal was to collect a large amount of clear audio recordings that will form a quality dataset. In further research, this dataset will be used for machine learning model training and testing, i.e. for developing a system for automatic recognition of the type of car engine based on fuel.

Keywords

acoustic based acquisition system, dataset, audio signals, internal combustion engines

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References


S. Das, A. Dey, A. Pal and N. Roy, "Applications of Artificial Intelligence in Machine Learning: Review and Prospect", Int. J. Comput. Appl., vol. 115, no. 9, pp. 31-41, April 2015.

P. Dhanalakshmi, S. Palanivel and V. Ramalingam, "Classification of Audio Signals Using SVM and RBFNN", Expert Syst. Appl., vol. 36, no. 3, part 2, pp. 6069-6075, 2009.

P. Dhanalakshmi, S. Palanivel and V. Ramalingam, "Classification of Audio Signals Using AANN and GMM", Appl. Soft. Comput., vol. 11, no. 1, pp. 716-723, 2011.

H. Ponce, P. Ponce and A. Molina, "Adaptive Noise Filtering Based on Artificial Hydrocarbon Networks: An Application to Audio Signals", Expert Syst. Appl., vol. 41, no. 14, pp. 6512-6523, 2014.

Z. Liu, J. Huang, Y. Wang and T. Chen, "Audio feature extraction and analysis for scene classification", In Proceedings of First Signal Processing Society Workshop on Multimedia Signal Processing, Princeton, NJ, USA, 23-25 June 1997, pp. 343-348.

T. Birtchnell, "Listening Without Ears: Artificial Intelligence in Audio Mastering", Big Data & Society, vol. 5, no. 2, July 2018.

G. P. Chossière, R. Malina, F. Allroggen, S. D. Eastham, R. L. Speth and S. R. H. Barrett, "Country- and Manufacturer-Level Attribution of Air Quality Impacts due to Excess NOx Emissions from Diesel Passenger Vehicles in Europe", Atmospheric Environ., vol. 189, pp. 89-97, Sept. 2018.

M. Milivojčević, F. Pantelić, D. Ćirić, "Pozicioniranje mikrofona prilikom snimanja audio karakteristika motora putničkih vozila" (Microphone positioning when recording audio characteristics of passenger car engines) In Proceedings of 63rd National Conference on Electrical, Electronic and Computing Engineering ETRAN, Srebrno Jezero, Serbia: 3-6 June 2019, pp. 58-62 (in Serbian).

M. Milivojčević, F. Pantelić and D. Ćirić, "Comparison of frequency characteristics of sound generated by internal combustion engines depending on fuel", In Proceedings of 26th Noise and Vibration, Niš, Serbia: 6-7 December 2018, pp. 115-120.

N. Evans, Automated Vehicle Detection and Classification using Acoustic and Seismic Signals. Ph.D. Thesis, University of York, 2010.

H. Frederick, A. Winda and M. Iwan Solihin, "Automatic petrol and diesel engine sound identification based on machine learning approaches", In Proceedings of the International Conference on Automotive, Manufacturing, and Mechanical Engineering. Bali, Indonesia: 26-28 September 2018, published at E3S Web of Conferences, vol. 130, article no. 01011.

A. D. Mayvana, S. A. Beheshtib and M. H. Masoom, "Classification of Vehicles Based on Audio Signals using Quadratic Discriminant Analysis and High Energy Feature Vectors", Int. J. Soft Comput., vol. 6, no. 1, pp. 53-64, Feb. 2015.

A. Wieczorkowska, E. Kubera, T. Słowik and K. Skrzypiec, "Spectral Features for Audio Based Vehicle and Engine Classification", J. Intell. Inf. Sys., vol. 50, pp. 265-290, 2018.

E. Alexandre, L. Cuadra, S. Salcedo-Sanz, A. Pastor-Sánchez and C. Casanova-Mateo, "Hybridizing Extreme Learning Machines and Genetic Algorithms to Select Acoustic Features in Vehicle Classification Applications", Neurocomput., vol. 152, pp. 58-68, March 2015.

S. D. Badiger and M. UttaraKumari, "Vehicle Classification Using Machine Learning Algorithms Based on the Vehicular Acoustic Signature", Sci. Tech. Dev., vol. 8, no. 11, pp. 369-374, Nov. 2019.

Ultrasonic Waterproof Range Finder datasheet. Available at: https://www.jahankitshop.com/getattach.aspx?id=4635&Type=Product.

A. Pajankar, Kickstart to Arduino Nano. Susteren, The Netherlands: Elektor International Media, 2022.

B. R. Kent, Science and Computing with Raspberry Pi. San Rafael, USA: Morgan & Claypool Publishers, 2018.

C562 CM specifications. Available at: https://www.akg.com/Microphones/Boundary%20Layer%20Microphones/C562CM.html.

Digital high definition microphone interface specifications. Available at: https://www.ikmultimedia.com/products/irigprehd/.

S. Amiriparian, M. Gerczuk, S. Ottl, N. Cummins, M. Freitag, S. Pugachevskiy, A. Baird and B. Schuller, "Snore sound classification using image-based deep spectrum features", In Proceedings of Interspeech 2017, Stockholm, Sweden, August 20–24, 2017, pp. 3512-3516.

D. Ćirić, Z. Perić, J. Nikolić, N. Vučić, "Audio signal mapping into spectrogram-based images for deep learning applications", In Proceedings of 20th International Symposium Infoteh-Jahorina (INFOTEH), East Sarajevo, Bosnia and Herzegovina: March 17-19, 2021, pp. 1-6.


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ISSN: 0353-3670 (Print)

ISSN: 2217-5997 (Online)

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