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

2. 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 2

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

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5.  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. 5 ∑

6. 6 Adaptive System IdentificationAdaptive Noise Cancellation Adaptive PredictionAdaptive Inverse ∑∑ ∑

7.  Cost Function  Wiener-Hopf equation ◦ D  Least Mean Square (LMS)  Recursive Least Square (RLS) 7

8.  Widrow-Hoff LMS Algorithm ◦ ◦ d 8

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

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13.  MATLAB/Simulink  Xilinx System Generator 13  Xtreme DSP development kit:  FPGA device (Virtex4 xC4SX35-10FF668)  Two 14- bit DAC onboard channels  Ultrasound Data  SignalWave DSP/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  Hardware  Design Tools

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

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

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

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

20. 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

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24. 24 Description: L = 6 Adaptive FIR Filter

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

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41. 41 Description: L =10 Adaptive FIR Filter

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

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47.  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. 47

48.  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