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Active Noise Cancellation

Active Noise Cancellation. Jessica Arbona & Christopher Brady Dr. In Soo Ahn & Dr. Yufeng Lu, Advisors. Outline . Goal Adaptive Filter Adaptive Filtering System Four Typical Applications of Adaptive Filters How does the Adaptive Filter Work? Project Description High Level Flowchart

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Active Noise Cancellation

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  1. Active Noise Cancellation Jessica Arbona & Christopher Brady Dr. In Soo Ahn & Dr. Yufeng Lu, Advisors

  2. Outline • Goal • Adaptive Filter • Adaptive Filtering System • Four Typical Applications of Adaptive Filters • How does the Adaptive Filter Work? • Project Description • High Level Flowchart • Equipment List • Design Approach • Procedure • MATLAB Simulation (Speech Data) • Hardware Design (Ultrasound Data) • FIR filter structures (Ultrasound Data) • DSP/FPGA Implementation (Speech Data) • Demonstration • Conclusion

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

  4. Adaptive Filter

  5. Adaptive Filtering System • The adaptive filtering system contains four signals: reference signal, d(n), input signal, x(n), output signal, y(n), and the error signal, e(n). The filter, w(n), adaptively adjusts its coefficients according to an optimization algorithm driven by the error signal. ∑

  6. Four Typical Applications of Adaptive Filters ∑ ∑ Adaptive System Identification Adaptive Noise Cancellation ∑ Adaptive Prediction Adaptive Inverse

  7. How does the Adaptive Filters Work? • Cost Function • Wiener-Hopfequation • D • Least Mean Square (LMS) • Recursive Least Square (RLS)

  8. LMS implementation • Widrow-Hoff LMS Algorithm • d

  9. Convergence of LMS • µ is the step size • µ must be determined in for the system to converge • f

  10. RLS implementation

  11. Project Description

  12. High Level Block Diagram ∑

  13. Hardware Equipment Lists • Design Tools • MATLAB/Simulink • Xilinx System Generator • XtremeDSP development kit: • FPGA device (Virtex4 xC4SX35-10FF668) • Two 14- bit DAC onboard channels • Ultrasound Data • SignalWaveDSP/FPGA board • Audio CODEC (sampling frequency varies from 8kHZ to 48kHZ) • Real-time workshop and Xilinx system generator in MATLAB/Simulink • TI DSP (TMS320C6713) and Xilink Virtex II FPGA (XC2V300- FF1152) • Speech Data

  14. Design Approach • Simulation • MATLAB • Least Mean Square (LMS) • Recursive Least Square (RLS) • Hardware • Least Mean Square • Design • Test FIR filter structures • Implement

  15. Procedure

  16. MATLAB Simulation(Speech Data)

  17. Design Description • Speech Data Processing • MATLAB simulation with Tap (L) = 10 • LMS • RLS • Speech Data • Recorded Voice Signal • Recorded Engine Noise

  18. Noise and Desired Signals Figure 1: Desired Signal Figure 3: Reference Signal Figure 2: Noise Signal

  19. RLS & LMS Filters : Coefficients • LMS • RLS Figure 4: LMS Filter Coefficients Figure 5: RLS Filter Coefficients

  20. Desired and Recovered Signals: L = 10 • LMS • RLS Figure 8: Desired Signal and Recovered Signal Figure 9: Desired Signal and Recovered Signal • Green – Desired Signal • Blue – Recovered Signal

  21. Hardware Design (Ultrasound Data)

  22. Xilinx Model

  23. Xilinx Model

  24. Adaptive Filter Design Description: • L = 6 • Adaptive FIR Filter

  25. Adaptive Filter Design

  26. Adaptive Coefficients

  27. FIR Filter Structure

  28. Desired and Recovered Signals: L = 10 • XtremeDSP- Virtex 4 • Hardware Results Orange – Input signal Blue – Output Signal

  29. FIR filter structures (Ultrasound Data)

  30. Standard Form

  31. Standard Form

  32. Transpose Form

  33. Transpose Form

  34. Systolic Form

  35. Systolic Form

  36. Systolic Pipeline FIR

  37. Systolic Pipeline FIR

  38. DSP/FPGA Implementation(Speech Data)

  39. LMS Xilinx Design for the Signal Wave Board

  40. LMS Xilinx Design for the Signal Wave Board

  41. Overall Design of the Adaptive Filter Description: • L =10 • Adaptive FIR Filter

  42. Overall Design of the Adaptive Filter

  43. Adaptive Filter Design

  44. FIR Filter Design

  45. Desired and Recovered Signals Figure 12: Desired Signal and Recovered Signal Figure 13: Spectrum of Desired and Recovered Signals

  46. Demonstration

  47. Conclusion • The adaptive filter is successfully simulated in MATLAB using various types of noise. The simulation results show a 24 dB reduction in the mean square error. These results are used in developing the Xilinx model of the system. After the system is successfully designed, alternative FIR structures are investigated in an attempt to improve efficiency. The standard FIR structure is found to be better suited for hardware implementation on a DSP/FPGA board.

  48. Reference • The adaptive filter is successfully simulated in MATLAB using various types of noise. The simulation results show a 24 dB reduction in the mean square error. These results are used in developing the Xilinx model of the system. After the system is successfully designed, alternative FIR structures are investigated in an attempt to improve efficiency. The standard FIR structure is found to be better suited for hardware implementation on a DSP/FPGA board.

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