1. Active Noise Cancellation System
Students:
Jessica Arbona & Christopher Brady
Advisors:
Dr. Yufeng Lu

2. Outline Goal Adaptive Filters
What is an adaptive filter?
Four Typical Application of Adaptive Filter
How Adaptive Filters works
Ultrasound Data
Data Collection
Filter Results
Speech Data
Filter Simulation
Summary
Future Plans

3. Goal
The goal of the project is to design and implement an active noise cancellation system using an adaptive filter.

4. What is an Adaptive Filter?
An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal.

5. Four Typical Applications of Adaptive Filter
Adaptive System Identification
Adaptive Noise Cancellation
Adaptive Prediction
Adaptive Inverse

6. How Adaptive Filters Works
Cost Function
Wiener-Hopf equation
Least Mean Square (LMS)
Recursive Least Square (RLS)

7. LMS implementation
Widrow-Hoff LMS Algorithm

8. Convergence of LMS

9. RLS implementation

10. Ultrasound Data Processing
Ultrasonic Measurement System

11. Hardware Upload the Variables to the Design Loading the Save Workspace

12. Variable.m

13. Xilinx’s block- ROM

14. Loading the Variables

15. Hardware Design without Adaptive Filter

16. Preliminary Results
Hardware Simulation
Software Simulation

17. Preliminary Results XtremeDSP- Virtex 4 Hardware Simulation X Signal
Y Signal

18. Hardware Design with Adaptive Filter

19. Hardware Design of the Adaptive Filter

20. Tap

21. XtremeDSP Development Kit – Virtex-4 Edition
Key Features:
Xilinx Devices
Two Independent DAC Channels
Support for external clock, on board oscillator

22. Progressive Results of the Input Signal [x] & Output Signal [y]
XtremeDSP- Virtex 4
Simulation

23. Speech Data Processing
MATLAB simulation with L = 10
LMS
RLS
MATLAB simulation with L = 7

24. Speech Data
Recorded Voice Signal
Recorded Engine Noise

25. Noise and Desired signal
Figure 1: Desired Signal
Figure 3: Reference Signal
Figure 2: Noise Signal

26. Spectral Analysis of Noise and Desired
Figure 4: Spectrum of Desired Signal
Figure 6: Spectrum of Reference Signal
Figure 5: Spectrum of Noise Signal

27. LMS filter coefficients

28. Desired and Recovered signal from LMS
Figure 7: Desired Signal and Recovered Signal
Figure 8: Spectrum of Desired and Recovered Signals

29. RLS Filter Coefficients with L = 10

30. Desired and Recovered signal from RLS with L = 10
Figure 9: Desired Signal and Recovered Signal
Figure 10: Spectrum of Desired and Recovered Signals

31. RLS Filter Coefficients with L = 7

32. Desired and Recovered from RLS with L = 7
Figure 11: Desired Signal and Recovered Signal
Figure 12: Spectrum of Desired and Recovered Signals

33. Summary Completed To Be complete Speech data simulation
LMS
RLS
LMS hardware implementation.
To Be complete
How mu changes the system performance
Comparison of Different FIR filter structure
Implement on SignalWave board
Hardware calculation for mu value
RLS hardware implementation

34. Schedule Fall Schedule Date Milestone Jessica Christopher
Jessica
Christopher
Thursday, November 17
Different FIR Form / Proposal
Work on Mu value / Proposal
Thursday, December 1
Different FIR Form
Work on Mu value
Spring Schedule
Thursday, January 19
Signal Wave Board
Research on Acoustic Noise Suppression
Thursday, January 26
Thursday, February 2
Hardware Calculation for Mu
Design and Simulate Noise Suppression System
Thursday, February 9
Thursday, February 16
RLS hardware Design with Matrix Inversion
Thursday, February 23
Testing of Noise Suppression System
Thursday, March 1
Thursday, March 8
Implementation Noise Suppression System
Thursday, March 22
Thursday, March 29
Thursday, April 5
Thursday, April 12
Preparing for Final Report
Thursday, April 19
Thursday, April 26

35. Reference
[1] D. Monroe, I. S. Ahn, and Y. Lu, “Adaptive filtering and target detection for ultrasonic backscattered signal”, IEEE International Conference on Electro/Information Technology, May 20-22, 2010, Normal, Illinois.

36. Questions?