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Hierarchical Classification of Environmental Noise Sources Considering the Acoustic Signature of Vehicle Pass-Bys

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Języki publikacji
EN
Abstrakty
EN
This work is focused on the automatic recognition of environmental noise sources that affect humans’ health and quality of life, namely industrial, aircraft, railway and road traffic. However, the recognition of the latter, which have the largest influence on citizens’ daily lives, is still an open issue. Therefore, although considering all the aforementioned noise sources, this paper especially focuses on improving the recognition of road noise events by taking advantage of the perceived noise differences along the road vehicle pass-by (which may be divided into different phases: approaching, passing and receding). To that effect, a hierarchical classification scheme that considers these phases independently has been implemented. The proposed classification scheme yields an averaged classification accuracy of 92.5%, which is, in absolute terms, 3% higher than the baseline (a traditional flat classification scheme without hierarchical structure). In particular, it outperforms the baseline in the classification of light and heavy vehicles, yielding a classification accuracy 7% and 4% higher, respectively. Finally, listening tests are performed to compare the system performance with human recognition ability. The results reveal that, although an expert human listener can achieve higher recognition accuracy than the proposed system, the latter outperforms the non-trained listener in 10% in average.
Rocznik
Strony
423--434
Opis fizyczny
Bibliogr. 32 poz., rys., tab., wykr.
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autor
autor
  • GTM – Grup de Recerca en Tecnologies Media, La Salle – Universitat Ramon Llull Quatre Camins 30, 08022 Barcelona, Catalonia, Spain, xvalero@salle.url.edu
Bibliografia
  • 1. Amman S.A., Das M. (2001), An efficient technique for modeling and synthesis of automotive engine sounds, IEEE Trans. Ind. Electron., 48, 225-234.
  • 2. Babisch W. (2006), Transportation noise and cardiovascular risk: Updated review and synthesis of epidemiological studies, Noise&Health, 8, 30, 1-29.
  • 3. Bishop C.M. (2003), Neural Networks for Pattern Recognition, Oxford Univ. Press, New York.
  • 4. Breiman L., Friedman J., Olshen R., Stone C. (1993), Classification and Regression Trees, Chapman&Hall, Boca Raton.
  • 5. Cevher V., Chellappa R., McClellan J.H. (2009), Vehicle speed estimation using acoustic wave patterns, IEE Transactions Signal Processing, 57, 1, 30-47.
  • 6. Chu S., Narayanan S., Jay Kuo C.-C. (2009), Environmental sound recognition with time-frequency audio features, IEEE Transactions Audio, Speech and Language Processing, 17, 2, 1142-1158.
  • 7. Couvreur C., Fontaine V., Gaunard P., Mubikangiey C.G. (1998), Automatic classification of environmental noise events by Hidden Markov Models, Applied Acoustics, 54, 3, 187-206.
  • 8. Cybenko G. (1989), Approximations by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303-314.
  • 9. Defréville B, Roy P., Rosin C., Pachet F. (2006), Automatic recognition of urban sound sources, Proceedings of the 120th AES Convention, Paris.
  • 10. Eronen A., Peltonen V., Tuomi J., Klapuri A., Fagerlund S., Sorsa T., Lorho G., Huopaniemi J. (2006), Audio-based context recognition, IEEE Transactions Audio, Speech, Lang. Processing., 14, 1, 321-329.
  • 11. EU Commission (1996), The Green Paper on Future Noise Policy, (COM(96) 540).
  • 12. EU Directive (2002), Directive 2002/49/EC of the European parliament and the Council of 25 June 2002 relating to the assessment and management of environmental noise, Official Journal of the European Communities, L 189/12, July 2002.
  • 13. Gygi B. (2001), Factors in the identification of environmental sounds, Ph.D. Thesis, Indiana University.
  • 14. ISO (2001), ISO/IEC FDIS 15938 4:2001, Information Technology Multimedia Content Description Interface - Part 4: Audio.
  • 15. ISO (2007), ISO 1996-2:2007 Acoustics - Description, measurement and assessment of environmental noise Part 2: Determination of environmental noise levels.
  • 16. Jones M.T. (2008), Artificial Intelligence - A Systems Approach, Infinity Science Press, Higham.
  • 17. Kim Y., Jeong S., Kim D. (2002), A GMM-based Target Classification Scheme for a Node in Wireless Sensor Network, IEICE Trans. Fundamentals/Commun./Electron/Inf&Syst., E85, 1.
  • 18. Kim H., Moreau N., Sikora T. (2005), MPEG-7 Audio and beyond. Audio content indexing and retrieval, [Ed.] John Willey & Sons Ltd., Chichester.
  • 19. Ntalampiras S., Potamitis I., Fakotakis N. (2008), Automatic Recognition of Urban Environmental Sound Events, Proceedings International Association for Pattern Recognition Workshop on Cognitive Information Processing, Santorini.
  • 20. Peeters B., Blokland G. (2007), The Noise Emmission Model for European Road Traffic, Deliverable 11 IMAGINE project, EU 6th FP.
  • 21. Planet S., Iriondo I., Martínez E., Montero J. (2008), True: an online testing platform for multimedia evaluation, Proceedings 2nd International Workshop on EMOTION: Corpora for Research on Emotion and Affect at LREC08, Marrakech.
  • 22. Rabaoui A., Lachiri Z., Ellouze N. (2004), Automatic Environmental Noise Recognition, Proceedings IEEE International Conference on Industrial Technology, Hammamet.
  • 23. Rabiner L., Juang B-H. (1993), Fundamentals of Speech Recognition, Prentice-Hall, Englewood Cliffs, NJ.
  • 24. Rabiner L. (1989), A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, 77, 2, 257-286.
  • 25. Rasche F. (2004), Arousal and aircraft noise - Environmental disorders of sleep and health in terms of leep medicine, Noise & Health, 6, 22, 15-26.
  • 26. Riedmiller M., Braun H. (1993), A direct adaptive method for faster backpropagation learning-RPROP algorithm, Proceedings IEEE International Conference on Neural Networks, Nagoya.
  • 27. Sandberg U., Ejsmont A.J. (2002), Tyre/Road Noise Reference Book, Kisa, Sweden.
  • 28. Sobreira Seoane M., Rodriguez Molares A., Alba Castro J.L. (2008), Automatic classification of traffic noise, Proceedings Acoustics'08, Paris.
  • 29. Umapathy K., Krishnan S., Jimaa S. (2005), Multigroup classification of audio signals using time-frequency parameters, IEEE Trans. Multimedia, 7, 2, 308-315.
  • 30. Valero X., Alı큑s F. (2010), Applicability of MPEG-7 low level descriptors to environmental sound source recognition, Proceedings 1st Euroregio Conference, Ljubjana.
  • 31. Valero X., Alı큑s F. (2011a), Comparison of machine learning technique for the automatic recognition of soundscapes, Proceedings Forum Acusticum'2011, Aalborg.
  • 32. Valero X., Alıs F. (2011b), Automatic monitoring of environmental noise sources, Proceedings Tecniacustica'2011, Caceres.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0026-0067
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