CMPUT 617 - Advanced Signal Processing for Computer Scientists

Overview

This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flow-graph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multivariate techniques; Wavelet Transform; Cepstral analysis, Wiener and Kalman Filters, and various applications. This course qualifies as a breadth requirement in theory.

Course syllabus (PDF, 26kb)

Objectives

• Introduction to advanced signal processing theory that can be applied to various projects involving multi-dimensional datasets
• Understand stochastic view of multi-dimensional signals and how to extract useful and reliable information from those signals

Course Work

• Problem Sets
• MATLAB Projects
• Final Exam