Graduate Exam Abstract
Neil Wachowski
Ph.D. Preliminary
November 12, 2012, 10:00 a.m.
ECE Conference Room C101B Engineering
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.
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The aforementioned source characterization tasks
are 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.
Adviser: Dr. Mahmood R. Azimi-Sadjadi
Co-Adviser: N/A
Non-ECE Member: Dr. F. Jay Breidt, Statistics
Member 3: Dr. Kurt Fristrup, ECE
Addional Members: Dr. Ali Pezeshki, ECE
Publications:
1. N. Wachowski and M.R. Azimi-Sadjadi, “Detection and classification of time-varying transient signals using sparse coefficient models,” IEEE Trans. Audio, Speech, and Lang. Process., in preparation.
2. N. Wachowski and M.R. Azimi-Sadjadi, “Characterization of multiple transient acoustical sources from time-transform representations,” IEEE Trans. Audio, Speech, and Lang. Process., submitted.
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
