EE 586L Advanced Digital Signal Processing Lab
EE 586L and EE 434L, Advanced DSP Laboratory, Spring 2009
Course Description: Real-time signal processing experiments and design projects using special purpose DSP processor. Focus on C programming for embedded platforms. Understand real-time processing system issues including constraints of embedded systems and complexity analysis for improved algorithm design.
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: Graduate level coursework in Signal Processing, for example EE 519, Speech Recognition and Processing for Multimedia, EE 522, Immersive Audio Signal Processing, EE 569, Introduction to Digital Image Processing, EE 596 Wavelets or EE 583, Adaptive Signal Proceesing. None of these courses are required.
Tel: (213) 740-2320
Fax: (213) 740-4651
Email: antonio DOT ortega AT sipi DOT usc DOT edu
- Lectures Friday, 9-10:00pm, B 18
- Lab hours Friday, 10:00-12:00pm and 1:00-3:00pm, B 18
- Lectures Monday, 3:30-4:30pm, B18
- Lab hours M-4:30-5:30pm, W-4:00-5:00pm, F-10:00am-12:00pm, F-1:00-3:00pm, B 18
- Sean McPherson
- Email: smcphers AT usc DOT edu
- Tel: (213) 740-4655
- Office Hours: During Lab Hours
- Joohyun (Peter) Cho
- Email: joohyunc AT usc DOT edu
- Office Hours: During Lab Hours
- Final Demonstration Will be held on the last day of class
During the first few weeks assignments will be given out weekly to facilitate learning C programming for the DSP system. Mini projects may also be assigned periodically during the initial weeks. Grades will be determined based on completion of the assignments, mini projects, and the end of semester design project.
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 Blackboard Website.
- Rulph Chassaing, Donald Reay - Digital Signal Processing and Applications with the TMS320C6713 and TMS320C6416 DSK, 2nd Edition. Book Website
Material Covered (Subject to Change)
- Week 1 Intro to boards and tools â€“ Book Ch. 1-2
- Week 2 Architecture â€“ FIR-IIR Filters
- Week 3 FFT â€“ Adaptive Filtering
- Week 4 Code optimization â€“ Multirate Filtering
- Week 5 Project discussion â€“ Ideas and initial feedback
- Week 6 Project proposals - Reviews
- Weeks 5-15 Project
- Week 10 Progress report + presentation
- Week 15 Project demos/presentations â€“ Final report
- Project requirements:
- Projects should be done in groups of 2-3.
- Each project must perform some signal processing task in real-time using either the fixed or floating point DSP board. Common examples are adaptive, video or audio signal processing.
- Project code should be optimized so that algorithms run efficiently on the DSP board.
- The final demonstration of your project should clearly show the desired effect of your algorithm.
- Reporting requirements: a final report and a class presentation.
- Project guidelines will be posted on Blackboard
Sample Project Topics - Organized by Areas
- Isolated Word Recognition
- Speaker identification
- Speech compression
- Audio Compression
- Active noise cancellation
- Pitch to MIDI conversion
- Speech/Music discrimination
- 3D Audio
- Blind acoustic source separation
- Video Compression
- Video Tracking
- Video Stitching
- Baseband Modem
Statement for Students with Disabilities
Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to me (or to TA) as early in the semester as possible. DSP is located in STU 301 and is open 8:30 a.m.--5:00 p.m., Monday through Friday. The phone number for DSP is (213) 740-0776.
Statement on Academic Integrity
USC seeks to maintain an optimal learning environment. General principles of academic honesty include the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect oneÃs own academic work from misuse by others as well as to avoid using anotherÃs work as oneÃs own. All students are expected to understand and abide by these principles. Scampus, the Student Guidebook, contains the Student Conduct Code in Section 11.00, while the recommended sanctions are located in Appendix A http://www.usc.edu/dept/publications/SCAMPUS/gov/
Students will be referred to the Office of Student Judicial Affairs and Community Standards for further review, should there be any suspicion of academic dishonesty. The Review process can be found at http://www.usc.edu/student-affairs/SJACS/.