Signal Processing
Winter 2019


Friday 1:30 - 4:30 p.m. in WLH 722


Geoffrey Chan
E-mail: chan [at] queensu [dot] ca
Tel: 613 533-2939
Office: WLH 517

Office Hours: Friday 4:30 - 5:30 p.m. or by appointment


This course covers statistical signal processing and machine learning, with applications in speech, biomedical, and communication signal processing. Topics are selected to suit the technical interests of the class. Topics typically covered include: spectral modeling, linear prediction, cepstral processing, hidden Markov models, Bayesian inference, linear models, support vector machines, and neural networks.

Background Preparation


Students taking this course should have a strong grounding in probability and random variables (ELEC 326, ELEC 861, reference books 7 & 8 below) and in basic digital signal processing (ELEC 421, the non-random signal processing part of reference book 1 below).  Proficiency in computer programming is essential.


ELEC 421 info: Motivations: Why study DSP?  and What is Signal Processing?


No textbook prescribed.  The reference books below are listed roughly in decreasing relevance to the materials in this course.


Marking Scheme (Tentative)


Homework 30%, project 40%, exam 30%


Please familiarize with the rules and policies on academic honesty

Reference Books

1. J.G. Proakis and D.G. Manolakis, "Digital Signal Processing: Principles, Algorithms and Applications;" 4th edition, Prentice Hall, 2007..


2. C.M. Bishop, "Pattern Recognition and Machine Learning," Springer, 2006.


3. Rabiner & Juang, "Fundamentals of Speech Recognition," Prentice Hall, 1993.


4. Huang, Acero, & Hon, "Spoken Language Processing," Prentice Hall, 2001.


5. S. Haykin, "Adaptive Filter Theory," 4th ed., Prentice Hall, 2002.


4. S.L. Marple, Jr., "Digital Spectral Analysis with Applications", Prentice Hall, 1987.


7. Gray & Davisson, "An Introduction to Statistical Signal Processing," 2004, downloadable from http://www-ee.stanford.edu/~gray/sp.pdf


8. Stark & Woods, "Probability and Random Processes with Applications to Signal Processing," 3rd ed., Prentice Hall, 2001.





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Queen's University, Kingston, Ontario