225A. Digital Signal Processing. (3) Three hours of lecture per week. Prerequisites: 123 and 126 or solid background in stochastic processes. Advanced techniques in signal processing. Stochastic signal processing, parametric statistical signal models, and adaptive filtering. Application to spectral estimation, speech and audio coding, adaptive equalization, noise cancellation, echo cancellation, and linear prediction.
Monson H. Hayes, Statistical Digital Signal Processing and Modeling, Wiley, 1996 (ISBN 0471594318) [homepage]
In addition, various resources (Web and handouts) will be used to supplement the text.
The course will generally follow the topics in the textbook (advanced 1-D filtering theory), supplemented by additional material. In class, we will focus on building intuition and supplementing the text with additional material, examples, and applications. This makes it particularly important that students complete the assigned readings in timely fashion to derive more value from the class. The following topics will supplement the book:
The course builds on the following mathematics, which it is assumed the student is familiar with:
David G. Messerschmitt [homepage]
259M Cory Hall
Office hours: Tu 11-12 am and Th 4- 5 pm or by email appointment
Email: messer
Please read the course announcements often. If it is posted there, you are presumed to have been informed about it. See the announcements for reading and homework assignments. The schedule includes important deadlines, such as exams and project due dates.
Students are also reminded of the Departmental Policy on Academic Dishonesty and are also urged to also read and abide by the professional ethics represented in the IEEE Code of Ethics. Especially relevant in the latter are the two guidelines:
The components of the course will be weighted as follows in the final grade. The final grades will be set by matching a curve to the final course averages.
|
Component |
Weight |
Comments |
|
Assignments
and computer exercises |
0% |
We don’t have the resources to
grade assignments, but nevertheless it is important that you complete them as
a learning tool. You are encouraged to talk to other students about the
homework solutions; it need not be done individually. |
|
First
midterm |
30% |
80 minute open-book
multiple-choice and problem-solving exam. |
|
Second
midterm |
30% |
80 minute open-book
multiple-choice and problem-solving exam. |
|
Project
|
40% |
You will divide into groups of
three to complete a project report that will explore and apply the techniques
of the paper to an application area related to the course. |
|
Exam |
Date
and time |
Location |
|
First
midterm |
March 17 @ 2-3:30 |
258 Dwinelle |
|
Second
midterm |
May 10 @ 2-3:30 |
258 Dwinelle |
|
Project
milestone 1 |
March 8 @ midnight |
|
|
Project
milestone 2 |
March 31 @ midnight |
|
|
Project
final report |
May 3 @ midnight |
|
The second half of the course will include a significant group project. See the project page for details [html].