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Active Noise Cancellation System

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

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Active Noise Cancellation System

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  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

  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 • RLS

  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 RLSwith 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 withL = 7 Figure 11: Desired Signal and Recovered Signal Figure 12: Spectrum of Desired and Recovered Signals

  33. Summary • Completed • 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

  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?

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