Joint-structured-sparsity-based classification for multiple-measurement transient acoustic signals

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 42, Issue 6, 2012, Pages 1586-1598

  Zhang, Haichao ^a   Zhang, Yanning ^a   Nasrabadi, Nasser M ^b   Huang, Thomas S ^c  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b U S ARMY RESEARCH LABORATORY   (United States)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Joint sparse representation; joint structured sparsity; multiple measurement transient acoustic signal classification

Indexed keywords

ACOUSTIC DATA; CLASSIFICATION ACCURACY; DISCRIMINATIVE CLASSIFIERS; GREEDY ALGORITHMS; MULTIPLE MEASUREMENTS; SPARSE CODES; SPARSE PRIOR; SPARSE REPRESENTATION; STRUCTURAL INFORMATION; TRANSIENT ACOUSTICS;

ACOUSTIC WAVES;

TRANSIENT ANALYSIS;

EID: 84869497089     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2012.2196038     Document Type: Article

References (52)

1. S. Sampan, T. Baumann, J. S. Bay, R. D. James, J. H. Reed, and J. F. Rossi, "Neural fuzzy techniques in vehicle acoustic signal classification," Virginia Polytechnic Institude and State University, Blacksburg, VA, Tech. Rep., 1997.
2. M. R. Azimi-Sadjadi, Y. Yang, and S. Srinivasan, "Acoustic classification of battlefield transient events using wavelet sub-band features," in Proc. SPIE Defense Secur. Symp., 2007, vol. 6562, p. 656 215.
3. B. Kaushik and D. Nance, "A review of the role of acoustic sensors in the modern battlefield," in Proc. 11th AIAA/CEAS Aeroacoust. Conf., Monterey, CA, 2005.
4. 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, Laurel, MD, Aug. 2009.
5. 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.
6. K. Huang and S. Aviyente, "Sparse representation for signal classification," in Proc. Adv. Neural Inf. Process. Syst., 2006, pp. 609-616.
7. R. Grosse, R. Raina, H. Kwong, and A. Y. Ng, "Shift-invariant sparse coding for audio classification," in Proc. 23rd Conf. Uncertainty Artif. Intell., Vancouver, BC, Canada, 2007.
8. J. Yang, K. Yu, Y. Gong, and T. Huang, "Linear spatial pyramid matching using sparse coding for image classification," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2009, pp. 1794-1801.
9. D. Salvati and S. Canazza, "Improvement of acoustic localization using a short time spectral attenuation with a novel suppression rule," in Proc. 12th Int. Conf. Digit. Audio Effects, 2009, pp. 150-156.
10. 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, Jul. 1995.
11. 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.
12. 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., 2009, pp. 2695-2700.
13. M. Elad,M. A. T. Figueiredo, and Y. Ma, "On the role of sparse and redundant representations in image processing," Proc. IEEE - Special Issue on Applications of Sparse Representation & Compressive Sensing, vol. 98, no. 6, pp. 972-982, Jun. 2010.
14. J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, "Sparse representation for computer vision and pattern recognition," Proc. IEEE - Special Issue on Applications of Sparse Representation & Compressive Sensing, vol. 98, no. 6, pp. 1031-1044, Jun. 2010.
15. H. Lee, A. Battle, R. Raina, and A. Y. Ng, "Efficient sparse coding algorithms," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 801-808.
16. E. J. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006. (Pubitemid 43174093)
17. D. L. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
18. J. Wright, A. Y. Yang, A. Ganesh, S. Shankar 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.
19. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Sparse subspace target detection for hyperspectral imagery," in Proc. SPIE - Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery, 2010, vol. 7695, pp. 769503-1-769503-8.
20. J. K. Pillai, V. M. Patel, and R. Chellappa, "Sparsity inspired selection and recognition of IRIS images," in Proc. 3rd IEEE Int. Conf. Biom., Technol., Syst., 2009, pp. 1-6.
21. J. Gemmeke and B. Cranen, "Using sparse representations for missing data imputation in noise robust speech recognition," in Proc. EUSIPCO, Lausanne, Switzerland, 2008.
22. R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight, "Sparsity and smoothness via the fused Lasso," J. Roy. Stat. Soc. B, vol. 67, no. 1, pp. 91-108, Feb. 2005. (Pubitemid 40167043)
23. H. Zou and T. Hastie, "Regularization and variable selection via the elastic net," J. Roy. Stat. Soc. B, vol. 67, no. 2, pp. 301-320, Apr. 2005. (Pubitemid 40465877)
24. J. Huang, T. Zhang, and D. Metaxas, "Learning with structured sparsity," in Proc. Int. Conf. Mach. Learn., 2009, pp. 417-424.
25. J. Liu and J. Ye, "Moreau-Yosida regularization for grouped tree structure learning," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 1-9.
26. R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, "Model-based compressive sensing," IEEE Trans. Inf. Theory, vol. 56, no. 4, pp. 1982- 2001, Apr. 2010.
27. T. Peleg, Y. C. Eldar, and M. Elad, "Exploiting statistical dependencies in sparse representations for signal recovery," IEEE Trans. Signal Process., vol. 55, no. 5, pp. 2286-2303, 2012.
28. M. Yuan and Y. Lin, "Model selection and estimation in regression with grouped variables," J. Roy. Stat. Soc. B, vol. 68, no. 1, pp. 49-67, Feb. 2006. (Pubitemid 43415335)
29. Y. C. Eldar and M. Mishali, "Block sparsity and sampling over a union of subspaces," in Proc. 16th Int. Conf. Digit. Signal Process., 2009, pp. 1-8.
30. E. van den Berg and M. P. Friedlander, "Joint-sparse recovery from multiple measurements," Dept. Comput. Sci., Univ. British Columbia, Vancouver, BC, Canada, Tech. Rep. 2009-07, 2009.
31. K. Engan, S. F. Cotter, B. Rao, and K. Kreutz-Delgado, "Sparse solutions to linear inverse problems with multiple measurement vectors," IEEE Trans. Signal Process., vol. 53, no. 7, pp. 2477-2488, Jul. 2005. (Pubitemid 41174440)
32. M. F. Duarte, S. Sarvotham, M. B. Wakin, D. Baron, and R. G. Baraniuk, "Joint sparsity models for distributed compressed sensing," in Proc.Workshop Signal Process. Adapt. Sparse Struct. Represent., Rennes, France, 2005.
33. M. Mishali and Y. C. Eldar, "Reduce and boost: Recovering arbitrary sets of jointly sparse vectors," IEEE Trans. Signal Process., vol. 56, no. 10, pp. 4692-4702, Oct. 2008.
34. M. Mishali and Y. C. Eldar, "Blind multiband signal reconstruction: Compressed sensing for analog signals," IEEE Trans. Signal Process., vol. 57, no. 3, pp. 993-1009, Mar. 2009.
35. C. M. Bishop, Pattern Recognition and Machine Learning. Berlin, Germany: Springer-Verlag, 2006.
36. N. Cristianini and J. Shawe-Taylor, Kernel Methods for Pattern Analysis. Cambridge, U.K.: Cambridge Univ. Press, 2004.
37. D. L. Donoho, "For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution," Commun. Pure Appl. Math., vol. 59, no. 6, pp. 797-829, Jun. 2004.
38. J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
39. W. Dai and O. Milenkovic, "Subspace pursuit for compressive sensing signal reconstruction," IEEE Trans. Inf. Theory, vol. 55, no. 5, pp. 2230- 2249, 2009.
40. D. Needell and J. A. Tropp, "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples," Appl. Comput. Harmon. Anal., vol. 26, no. 3, pp. 301-321, May 2009.
41. D. Malioutov, M. Cetin, and A. Willsky, "Sparse signal reconstruction perspective for source localization with sensor arrays," IEEE Trans. Signal Process., vol. 53, no. 8, pp. 3010-3022, Aug. 2005. (Pubitemid 41372811)
42. D. P. Wipf, J. P. Owen, H. T. Attias, K. Sekihara, and S. Nagarajan, "Robust Bayesian estimation of the location, orientation, and timecourse of multiple correlated neural sources using MEG," NeuroImage, vol. 49, no. 1, pp. 641-655, Jan. 2010.
43. A. Y. Yang, S. Maji, C. M. Christoudias, T. Darrell, J. Malik, and S. S. Sastry, "Multiple-view object recognition in band-limited distributed camera networks," in Proc. 3rd ACM/IEEE Conf. Distrib. Smart Cameras, 2009, pp. 1-8.
44. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Non-local sparse models for image restoration," in Proc. Int. Conf. Comput. Vis., 2009, pp. 2272-2279.
45. J. Mairal, M. Elad, and G. Sapiro, "Sparse representation for color image restoration," IEEE Trans. Image Process., vol. 17, no. 1, pp. 53-69, Jan. 2008.
46. S. Li and L. Fang, "An efficient learned dictionary and its application to non-local denoising," in Proc. Int. Conf. Image Process., 2010, pp. 1945- 1948.
47. J. A. Tropp, A. C. Gilbert, andM. J. Strauss, "Algorithms for simultaneous sparse approximation," EURASIP J. Appl. Signal Process., vol. 86, no. 3, pp. 589-602, Mar. 2006.
48. M. F. Duarte, V. Cevher, and R. G. Baraniuk, "Model-based compressive sensing for signal ensembles," in Proc. Allerton Conf. Commun., Control, Comput., 2009, pp. 244-250.
49. A. Rakotomamonjy, "Surveying and comparing simultaneous sparse approximation (or group lasso) algorithms," Dept. Phys., Univ. Rouen, Normandy, France, 2010, Tech. Rep.
50. E. Van Den Berg and M. P. Friedlander, "Theoretical and empirical results for recovery from multiple measurements," IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2516-2527, May 2010.
51. S. Engelberg and E. Tadmor, "Recovery of edges from spectral data with noise-a new perspective," SIAM J. Numer. Anal., vol. 46, no. 5, pp. 2620- 2635, 2008.
52. J. Ho,M. Yang, J. Lim, K. Lee, and D. Kriegman, "Clustering appearances of objects under varying illuminations conditions," in Proc. Int. Conf. Comput. Vis. Pattern Recogn., 2003, pp. I-11-I-18.