Graph-based sensor fusion for classification of transient acoustic signals

IEEE Transactions on Cybernetics

Volumn 45, Issue 3, 2015, Pages 562-573

  Srinivas, Umamahesh ^a   Nasrabadi, Nasser M ^b   Monga, Vishal ^a  

^a The Pennsylvania State University   (United States)

^b U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

Acoustic signal classification; discriminative graphs; multiple measurements; symbolic features

Indexed keywords

ACOUSTIC WAVES; CLASSIFICATION (OF INFORMATION); GRAPHIC METHODS; ROCKETS;

ACOUSTIC CLASSIFICATION; ACOUSTIC SIGNALS; DISCRIMINATIVE GRAPHS; MULTIPLE MEASUREMENTS; PROBABILISTIC GRAPHICAL MODELS; SIMULTANEOUS ACQUISITION; SYMBOLIC DYNAMIC FILTERING; SYMBOLIC FEATURES;

ACOUSTIC SIGNAL PROCESSING;

EID: 85027936433     PISSN: 21682267     EISSN: 21682275     Source Type: Journal    
DOI: 10.1109/TCYB.2014.2331284     Document Type: Article

References (39)

1. B. Kaushik, D. Nance, and K. K. Ahuja, "A review of the role of acoustic sensors in the modern battlefield," in Proc. 11th AIAA/CEAS Aeroacoust. Conf., Monterey, CA, USA, 2005, pp. 1-13.
2. M. R. Azimi-Sadjadi, Y. Yang, and S. Srinivasan, "Acoustic classification of battlefield transient events using wavelet sub-band features," Proc. SPIE, vol. 6562, pp. 656215.1-656215.8, 2007.
3. C. Clavel, T. Ehrette, and G. Richard, "Events detection for an audio-based surveillance system," in Proc. IEEE Int. Conf. Multimedia Expo, Amsterdam, The Netherlands, 2005, pp. 1306-1309.
4. G. Valenzise, L. Gerosa, M. Tagliasacchi, F. Antonacci, and A. Sarti, "Scream and gunshot detection and localization for audio-surveillance systems," in Proc. IEEE Conf. Adv. Video Signal Surveill., London, U.K., 2007, pp. 21-26.
5. S. Sampan, "Neural fuzzy techniques in vehicle acoustic signal classification,"Ph.D. thesis, Dept. Electr. Eng., Virginia Polytech. Inst. State Univ., Blacksburg, VA, USA, 1997.
6. M. E. Munich, "Bayesian subspace methods for acoustic signature recognition of vehicles," in Proc. Eur. Signal Process. Conf., 2004, pp. 2107-2110.
7. A. Klausner, S. Erb, A. Tengg, and B. Rinner, "DSP based acoustic vehicle classification for multi-sensor real-time traffic surveillance," in Proc. Eur. Signal Process. Conf., Poznan, Poland, 2007, pp. 1916-1920.
8. S. Z. Li, "Content-based classification and retrieval of audio using the nearest feature line method," IEEE Speech Audio Process., vol. 8, no. 5, pp. 619-625, Sep. 2000.
9. G. Guo and S. Z. Li, "Content-based audio classification and retrieval by support vector machines," IEEE Trans. Neural Netw., vol. 14, no. 1, pp. 209-215, Jan. 2003.
10. W.-H. Liao and Y.-K. Lin, "Classification of non-speech human sounds: Feature selection and snoring sound analysis," in Proc. Int. Conf. Syst., Man, Cybern., San Antonio, TX, USA, 2009, pp. 2695-2700.
11. L. Rabiner and B. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ, USA: Prentice Hall, 1993.
12. D. Hoiem, K. Yan, and R. Sukthankar, "SOLAR: Sound object localization and retrieval in complex audio environments," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2005, pp. 429-432.
13. D. Salvati and S. Canazza, "Improvement of acoustic localization using a short time spectral attenuation with a novel suppression rule," in Proc. Int. Conf. Digit. Audio Effects, Como, Italy, 2009, pp. 150-156.
14. R. E. Learned and A. S. Willsky, "A wavelet-packet approach to transient signal classification," Appl. Comput. Harmon. Anal., vol. 2, no. 3, pp. 265-278, 1995.
15. M. Till and S. Rudolph, "Optimized time-frequency distributions for acoustic signal classification," Proc. SPIE, vol. 4391, pp. 81-91, 2001.
16. A. Ganapathiraju, J. E. Hamaker, and J. Picone, "Applications of support vector machines to speech recognition," IEEE Trans. Signal Process., vol. 52, no. 8, pp. 2348-2355, Aug. 2004.
17. V. Mirelli, S. Tenney, Y. Bengio, N. Chapados, and O. Delalleau, "Statistical machine learning algorithms for target classification from acoustic signature," in Proc. MSS Battlespace Acoust. Magn. Sensors, 2009, pp. 1-18.
18. A. Kundu, G. C. Chen, and C. E. Persons, "Transient sonar signal classification using hidden Markov models and neural nets," IEEE J. Ocean. Eng., vol. 19, no. 1, pp. 87-99, Jan. 1994.
19. R. Grosse, R. Raina, H. Kwong, and A. Y. Ng, "Shift-invariant sparse coding for audio classification," in Proc. 23rd Conf. Uncertainty Artif. Intell., 2007, pp. 149-158.
20. H. Zhang, Y. Zhang, N. M. Nasrabadi, and T. S. Huang, "Joint-structured-sparsity-based classification for multiple-measurement transient acoustic signals," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 6, pp. 1586-1598, Dec. 2012.
21. J. Wright, A. Y. Yang, A. Ganesh, S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210-227, Feb. 2009.
22. D. L. Hall and J. Llinas, "An introduction to multisensor data fusion,"Proc. IEEE, vol. 85, no. 1, pp. 6-23, Jan. 1997.
23. P. K. Varshney, "Multisensor data fusion," Electron. Commun. Eng. J., vol. 9, no. 6, pp. 245-253, 1997.
24. nd ed. Boca Raton, FL, USA: CRC Press, 2008.
25. Y. Freund and R. E. Shapire, "A short introduction to boosting," J. Jpn. Soc. Artif. Intell., vol. 14, no. 5, pp. 771-780, Sep. 1999.
26. U. Srinivas, N. M. Nasrabadi, and V. Monga, "Graph-based multisensor fusion for acoustic signal classification," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Vancouver, BC, Canada, 2013, pp. 261-265.
27. S. L. Lauritzen, Graphical Models. Oxford, U.K.: Oxford Univ. Press, 1996.
28. M. J. Wainwright and M. I. Jordan, "Graphical models, exponential families and variational inference," Found. Trends Mach. Learn., vol. 1, nos. 1-2, pp. 1-305, 2008.
29. V. Y. F. Tan, S. Sanghavi, J. W. Fisher, and A. S. Willsky, "Learning graphical models for hypothesis testing and classification," IEEE Trans. Signal Process., vol. 58, no. 11, pp. 5481-5495, Nov. 2010.
30. V. Rajagopalan and A. Ray, "Symbolic time series analysis via wavelet-based partitioning," Signal Process., vol. 86, no. 11, pp. 3309-3320, 2006.
31. D. G. Childers, D. P. Skinner, and R. C. Kemerait, "The cepstrum: A guide to processing," Proc. IEEE, vol. 65, no. 10, pp. 1428-1443, Oct. 1977.
32. C. K. Chow and C. N. Liu, "Approximating discrete probability distributions with dependence trees," IEEE Trans. Inf. Theory, vol. 14, no. 3, pp. 462-467, Mar. 1968.
33. G. F. Cooper, "The computational complexity of probabilistic inference using Bayesian belief networks," Artif. Intell., vol. 42, no. 3, pp. 393-405, Mar. 1990.
34. H. L. V. Trees, Detection, Estimation, and Modulation Theory, Part IV, Optimum Array Processing. New York, NY, USA: Wiley, 2002.
35. T. Downs and A. Tang, "Boosting the tree augmented naive Bayes classifier," in Intelligent Data Engineering and Automated Learning, vol. 3177. Berlin, Germany: Springer, 2004, pp. 708-713.
36. M. P. Norton, Fundamentals of Noise and Vibration Analysis for Engineers. Cambridge, U.K.: Cambridge Univ. Press, 1989.
37. J. Ho, M. Yang, J. Lim, K. Lee, and D. Kriegman, "Clustering appearance of objects under varying illumination conditions," in Proc. Int. Conf. Comput. Vis. Pattern Recognit., 2003, pp. I-11-I-18.
38. S. Ferrari and A. Vaghi, "Demining sensor modeling and feature-level fusion by Bayesian networks," IEEE Sensors J., vol. 6, no. 2, pp. 471-483, Apr. 2006.
39. G. F. Hughes, "On the mean accuracy of statistical pattern recognizers,"IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55-63, Jan. 1968.