Give


Graduate Exam Abstract


Neil Wachowski

Ph.D. Final

December 20, 2013, 10:00 AM - 12:00 PM

ECE conference room

Characterization of Multiple Time-Varying Transient Sources from Multivariate Data


Abstract: This work considers the development of novel methods for automatically detecting, classifying, and estimating the signatures of highly variable transient sources of intrinsic and extrinsic sound within national park soundscapes. Monitoring stations are typically deployed for months at a time to constantly record acoustical events, where historically (due to storage limitations) a particular type of lossy time-transform representation is used in order to avoid storing raw audio waveforms. The goal is to prevent manual observation and evaluation of a large volume of data for detecting and classifying events produced by commonly occurring man-made (e.g. aircraft) and natural (e.g. weather effects) acoustical sources. <br><br> Automated source characterization is complicated by many factors including nonstationary source signatures, a large number of possible event types (signal and interference), variable number of sources that may be simultaneously present leading to superimposed signatures, variable event structure and duration even among those events associated with a single source type, unknown arrival times and Doppler shift, and the presence of noise as well as other environmental and operational variations. Since most applicable methods typically only address a subset of these complications, the objective is to develop comprehensive solutions that consider all the intricacies of natural acoustic scenes, while still remaining viable for a multitude of applications, e.g., speech recognition and battlefield surveillance. <br><br> Two primary solutions have been developed. In the first method, a set of likelihood ratio tests is hierarchically applied to each observation to detect and classify both signal and interference sources, that may be simultaneously present. Since the signatures of each acoustical event typically span several adjacent observations they are assumed to be dependent, and hence, an adaptive parameter estimation procedure is used to calculate the likelihood values. The second method evaluates the likelihood of observing a sequence of elementary atom coefficient states, that are extracted from the original vector observation sequence, given a parameter set for each signal type. This framework remains robust to interference, and indeed is capable of separating signal and interference signatures, by exploiting sparsity that is inherently present in the data. The experimental results of applying each method to the problem of characterizing national park soundscapes attest to their effectiveness at performing the mentioned tasks.

Adviser: Dr. Mahmood Azimi
Co-Adviser: N/A
Non-ECE Member: Dr. F. Jay Breidt, Statistics
Member 3: Dr. Ali Pezeshki, ECE
Addional Members: Dr. Kurt Kristrup, ECE

Publications:
1. N. Wachowski and M.R. Azimi-Sadjadi, "Detection and classification of nonstationary transient signals using sparse approximations and Bayesian networks," IEEE Trans. Audio, Speech, and Lang. Process., to be summited, December 2013.

2. N. Wachowski and M. Azimi-Sadjadi, "Characterization of multiple transient acoustical sources from time-transform representations," IEEE Trans. Audio, Speech, and Lang. Process., vol. 21, no. 9, pp. 1966-1978, September 2013.

3. N. Wachowski and M.R. Azimi-Sadjadi, "A new synthetic aperture sonar processing method using coherence analysis," IEEE Journal of Oceanic Engr., vol. 36, no. 4, pp. 665-678, October 2011.

4. J. Cartmill, N. Wachowski, and M. R. Azimi-Sadjadi, "Buried underwater object classification using a collaborative multiaspect classifier," IEEE Journal of Oceanic Engr., vol. 34, no. 1, pp. 32-44, January 2009.

5. Y. Zhao, A. Dinstel, M.R. Azimi-Sadjadi, and N. Wachowski, "Localization of near-field sources in sonar data using the sparse representation framework," Proc. of MTS/IEEE Oceans, pp. 1-6, September 2011.

6. N. Wachowski, M.R. Azimi-Sadjadi, and R. Holtzapple, "SAS-like acoustic color processing for a single-hydrophone sonar platform," Proc. of MTS/IEEE Oceans, pp. 1-8, September 2010.

7. M.R. Azimi-Sadjadi and N. Wachowski, "An information theoretic approach for in-situ underwater target classification," Proc. of the International Joint Conference on Neural Networks (IJCNN), pp. 1-8, July 2010.

8. Y. Zhao, N. Wachowski, and M.R. Azimi-Sadjadi, "Target coherence analysis using canonical correlation decomposition for SAS data," Proc. of MTS/IEEE Oceans, pp. 1-7, October 2009.

9. Y. Zhao, M.R. Azimi-Sadjadi, N. Wachowski, and N. Klausner, "Spatial correlation analysis using canonical correlation decomposition for sparse sonar array processing," IEEE International Conference on Systems, Man, and Cybernetics, pp. 2739-2744, October 2009.

10. N. Wachowski, and M.R. Azimi-Sadjadi, "A likelihood-based decision feedback system for multi-aspect classification of underwater targets," Proc. of the IJCNN, pp. 3232-3239, June 2009.

11. N. Wachowski, and M.R. Azimi-Sadjadi, "Buried underwater object classification using frequency subband coherence analysis," Proc. of MTS/IEEE Oceans, pp. 1-8, September 2008.

12. J. Cartmill, N. Wachowski, and M.R. Azimi-Sadjadi, "Buried underwater object classification using a collaborative multi-aspect classifier," Proc. of the IJCNN, pp. 1807-1812, August 2007.

13. N. Wachowski, J. Cartmill, and M.R. Azimi-Sadjadi, "Underwater target classification using the wing BOSS and multi-channel decision fusion," Proc. Of SPIE Defense and Security, Vol. 6553, pp. 65530Q.1-65530Q.10, April 2007.


Program of Study:
ECE 516
ECE 521
ECE 530
ECE 752
ECE 795
STAT 605
ECE 512
ECE 513