ARAÇ İÇİ SESİNDEN ARACI TANIMA VE SINIFLANDIRMA

Year 2021, Volume: 9 Issue: 1, 129 - 136, 02.03.2021

Emrah Aydemir , Murat Işık

https://doi.org/10.36306/konjes.755710

Abstract

Günümüzde, teknolojik imkanların hızla gelişmesiyle ses sınıflandırma uygulamalarının sayıları da artmakta ve popüler bir çalışma alanı haline gelmektedir. Bu çalışmada, amacımız durağan halde bir aracın üretmiş olduğu sesi kullanarak "aracın sesli imzasını" üretmek ve aracın sınıflandırılması için kullanmaktır. Çalışan bir aracın sesi; motor sesi, titreşimden kaynaklı sesler, rüzgâr sesleri gibi bazı seslerin bir araya gelmesiyle oluşur. Uygulamada 22 aracın rölantideki sesleri kaydedilmiş ve Local Binary Pattern (LBP) ve Cubic SVM algoritmaları kullanılarak %95,2 oranında başarılı sınıflandırılmıştır. Ayrıca, elde edilen sonuçlar literatürdeki çalışmalarla karşılaştırılmıştır.

Keywords

ses sınıflandırma, araç tanıma, sesli imza, araç sınıflandırma

Vehicle Detection and Classification from Its Interior Sound

Abstract

Today, with the rapid development of technological possibilities, the number of sound classification applications are increasing and becoming a popular field for researchers. In this study, our aim is to extract "vehicle sound signature" by using the sound produced by the vehicle at idle mode. After that to use this sound signature for the classification of the vehicle. The sound of a working vehicle at idle mode consist of some noises cause by engine, vibration, wind etc. In practice, the sounds of 22 vehicles at idle mode were recorded and 95.2% successful classification was made by using the Local Binary Pattern (LBP) method and the Cubic SVM algorithm. In addition, the results were analyzed by comparing them with similar studies in the related literature.

Keywords

sound classification, vehicle recognition, voice signature, vehicle classification

References

• Alexandre, E., Cuadra, L., Salcedo-Sanz, S., Pastor-Sánchez, A., Casanova-Mateo, C., 2015, “Hybridizing extreme learning machines and genetic algorithms to select acoustic features in vehicle classification applications”, Neurocomputing, 152, 58-68.
• Bisio, I., Garibotto, C., Grattarola, A., Lavagetto, F., Sciarrone, A., 2018, “Smart and robust speaker recognition for context-aware in-vehicle applications”, IEEE Transactions on Vehicular Technology, 67(9), 8808-8821.
• Chu, S., Narayanan, S., Kuo, C.C.J., 2008, "Environmental sound recognition using MP-based features," IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1-4, 2008.
• Cowling, M., Sitte, R., 2003, “Comparison of techniques for environmental sound recognition”, Pattern Recognition Letters, vol. 24, pp. 2895–2907.
• Da Costa, M.V.B., Couto, C.M.V., Couto, L.N., 2019 October, “Face Recognition Using LBP on an Image Transformation Based on Complex Network Degrees”, 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 163-169.
• Dalir, A., Beheshti, A.A., Masoom, M.H., 2018, “Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors”, arXiv, arXiv:1804.01212.
• De Angelis, G., De Angelis, A., Pasku, V., Moschitta, A., Carbone, P., 2016 October, “A simple magnetic signature vehicles detection and classification system for smart cities”, IEEE International Symposium on Systems Engineering (ISSE), pp. 1-6.
• Dokur, Z., Ölmez, T., 2008, “Heart sound classification using wavelet transform and incremental self- organizing map”, Digital Signal Processing, 18(6), 951-959.
• Erb, S., 2007, “Classification of vehicles based on acoustic features”, Doctoral Dissertation Thesis, Graz University of Technology, Austria.
• George, J., Cyril, A., Koshy, B. I., Mary, L., 2013, “Exploring sound signature for vehicle detection and classification using ANN”, International Journal on Soft Computing, 4(2), 29.
• George, J., Mary, L., Riyas, K. S., 2013 December, “Vehicle detection and classification from acoustic signal using ANN and KNN”. International Conference on Control Communication and Computing (ICCC), pp. 436-439.
• Ghiurcau, M.V., Rusu, C., 2009 July, “Vehicle sound classification. Application and low pass filtering influence”, International Symposium on Signals, Circuits and Systems, pp. 1-4.
• Hahn, D.A., Munir, A., Behzadan, V., 2019, “Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges”, IEEE Intelligent Transportation Systems Magazine.
• Han, W., Coutinho, E., Ruan, H., Li, H., Schuller, B., Yu, X., Zhu, X., 2016, “Semi-supervised active learning for sound classification in hybrid learning environments”, PloS one, 11(9).
• Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L., 2011, “Local binary patterns and its application to facial image analysis: a survey”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(6), 765-781.
• Huang, R., Hansen, J.H.L., 2006, "Advances in unsupervised audio classification and segmentation for the broadcast news and NGSW corpora," IEEE Transactions on Audio, Speech, and Language Processing, 14(3): pp. 907- 919.
• Jarnicki, J., Mazurkiewicz, J., Maciejewski, H., 1998, “Mobile object recognition based on acoustic information”, Proc. of the 24th Annual Conf. of the IEEE Industrial Society, vol. 3, pp. 1564–1569.
• Johnstone, M.N., Woodward, A., 2013 December, “Automated detection of vehicles with machine learning”, 11th Australian Information Security Management Conference, DOI: 10.4225/75/57b65924343cd.
• Kobayashi, T., Ye, J., 2014 May, “Acoustic feature extraction by statistics based local binary pattern for environmental sound classification”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3052-3056.
• Kubera, E., Wieczorkowska, A., Skrzypiec, K., 2015 October, “Audio-based hierarchic vehicle classification for intelligent transportation systems”, In International Symposium on Methodologies for Intelligent Systems, pp. 343-352, Springer, Cham.
• Lavner, Y., Ruinskiy, D., 2009, “A Decision-Tree-Based Algorithm for Speech/Music Classification and Segmentation,” EURASIP Journal on Audio, Speech, and Music Processing, doi:10.1155/2009/239892
• Lin, Y.L., Wei, G., 2005, “Speech emotion recognition based on HMM and SVM”, International conference on machine learning and cybernetics, 8, pp. 4898-4901.
• Mayvan, A.D., Beheshti, S.A., Masoom, M.H., 2015, “Classification of vehicles based on audio signals using quadratic discriminant analysis and high energy feature vectors”, International Journal on Soft Computing, 6(1), 53.
• Mielke, M., Brück, R., 2013 June, “Smartphone application for automatic classification of environmental sound”, the 20th International Conference Mixed Design of Integrated Circuits and Systems-MIXDES, pp. 512-515.
• Montino, P., Pau, D., 2019 September, “Environmental Intelligence for Embedded Real-time Traffic Sound Classification”, IEEE 5th International forum on Research and Technology for Society and Industry (RTSI), pp. 45-50.
• Paulraj, M.P., Adom, A.H., Sundararaj, S., Rahim, N.B.A., 2013, “Moving vehicle recognition and classification based on time domain approach”, Procedia Engineering, 53, 405-410.
• Pietikäinen, M., 2010, “Local Binary Patterns”, Scholarpedia, 5(3), 9775.
• Radhakrishnan, R., Divakaran, A., Smaragdis, P., 2005, “Audio analysis for surveillance applications”, IEEE WASPAA’05, pp. 158–161.
• Randall, R.B., 1987, “Frequency analysis”, California Üniversitesi, Bruel ve Kjaer, ISBN: 87-87355-07-8.
• Salamon, J., Bello, J.P., 2015 April, “Unsupervised feature learning for urban sound classification”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 171-175.
• Salamon, J., Bello, J.P., 2017, “Deep convolutional neural networks and data augmentation for environmental sound classification”, IEEE Signal Processing Letters, 24(3), 279-283.
• Salamon, J., Jacoby, C., Bello, J. P., 2014 November, “A dataset and taxonomy for urban sound research”, the 22nd ACM international conference on Multimedia, pp. 1041-1044.
• Scheirer, E., Slaney, M., 1997, "Construction and evaluation of a robust multifeature speech/music discriminator," IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1331- 1334.
• Tan, Y., Xu, Y., Das, S., Chaudhry, A., 2018 October, “Vehicle Detection and Classification in Aerial Imagery”, 25th IEEE International Conference on Image Processing (ICIP), pp. 86-90.
• Thwe, K.Z., War, N., 2017 June, “Environmental sound classification based on time-frequency representation”, 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 251-255.
• Wieczorkowska, A., Kubera, E., Słowik, T., Skrzypiec, K., 2018, Spectral features for audio based vehicle and engine classification, Journal of Intelligent Information Systems, 50(2), 265-290.
• Wu, H., Siegel, M., Khosla, P., 1998 May, “Vehicle sound signature recognition by frequency vector principal component analysis”, IEEE Instrumentation and Measurement Technology Conference, vol. 1, pp. 429-434.
• Yoo, I.-C., Yook, D., 2008, “Automatic sound recognition for the hearing impaired”, IEEE Trans. on Consumer Electronic, vol. 54, pp. 2029– 2036.
• Zhang, T., Kuo, C.C.J., 2001, "Audio content analysis for online audiovisual data segmentation and classification", IEEE Transactions on Speech and Audio Processing, 9(4): pp. 441 - 457.