EE 586L Advanced Digital Signal Processing Lab

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EE 586L and EE 434L, DSP Laboratory, Spring 2009

Course Description:

Prerequisites: EE 483, Introduction to Digital Signal Processing, or equivalent course and some course in C Programming. Please revisit material from both of these courses early on in the semester.

Recommended Preparation: EE 596, Wavelets, and EE 569, Introduction to Digital Image Processing. None of these courses are required.


Antonio Ortega

Signal and Image Processing Institute
Department of Electrical Engineering
University of Southern California
3740 McClintock Ave., EEB 436
Los Angeles, CA 90089-2564

Tel: (213) 740-2320
Fax: (213) 740-4651
Email: antonio DOT ortega AT sipi DOT usc DOT edu


EE 586L

  • Lectures Friday, 9-10:00pm, B 18
  • Lab hours Friday, 10:00-12:00pm and 1:00-3:00pm, B 18

EE 434L

  • Lectures Monday, 3:30-4:30pm, B18
  • Lab hours M-4:30-5:30pm, W-4:00-5:30pm, F-3:30-5:00pm, B 18

Teaching Assistants

    • Sean McPherson
    • Email: smcphers AT usc DOT edu
    • Tel: (213) 740-4655
    • Office Hours: During Lab Hours
  • Presentations
  • Final Demonstration Will be held on the last day of class


Blackboard Access

This semester I will use the Blackboard system to post assignments and solutions, as well as grades. Please register with Blackboard and create your Blackboard profile as soon as possible by following the instructions on the Blackboard Webpage [1].



  • Rulph Chassaing, Donald Reay - Digital Signal Processing and Applications with the TMS320C6713 and TMS320C6416 DSK, 2nd Edition. [2]


Material Covered (Subject to Change)

  • Week 1 Intro to boards and tools – Book Ch. 1-2
  • Week 2 Architecture – FIR-IIR Filters
  • Week 3 FFT – Adaptive Filtering
  • Week 4 Code optimization – Multirate Filtering
  • Week 5 Project discussion – Ideas and initial feedback
  • Week 6 Project proposals - Reviews
  • Weeks 5-13 Project
  • Week 10 Progress report + presentation
  • Week 14 Project demos/presentations – Final report


  • Project requirements:
    • Projects should be done in groups of 2-3.
    • Each project must involve using the wavelet transform as a tool. A signal is analyzed/classified, etc by computing its wavelet transform and then the required task (e.g. denoising/classification) is performed in the transform domain.
    • The Matlab toolbox or C libraries can be used for the project. C libraries are available at Dartmouth and Rutgers.
    • Whichever method is used, the source code will have to be made available along with the project report (only for the routines that you write, which could call those available in matlab or C.)
  • Reporting requirements: a final report and a class presentation.
  • Project descriptions and references
  • Test data for the projects
  • Software packages

Demos on the web

Sample Project Topics - Organized by Areas

  • Speech
    • Isolated Word Recognition
    • Speaker identification
    • Speech compression
      • LPC
      • ADPCM
  • Audio
    • Audio Compression
    • Active noise cancellation
    • Pitch to MIDI conversion
    • Speech/Music discrimination
    • 3D Audio
    • Blind acoustic source separation
  • Video
    • Video Compression
    • Video Tracking
    • Video Stitching
  • Communications
    • Baseband Modem