active noise cancellation

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

• 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

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

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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.

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∑

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Adaptive System IdentificationAdaptive Noise Cancellation

Adaptive Prediction Adaptive Inverse

∑ ∑

NoiseFIR

AdaptiveFilter

AdaptiveFilter

Algorithme(n)

y(n)

d(n)

Delay x(n) ∑

Cost Function

Wiener-Hopf equation◦D

Least Mean Square (LMS) Recursive Least Square (RLS)

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dXXXopt rRf 1

)}({ 2 neEJ

Widrow-Hoff LMS Algorithm◦

◦

◦ d

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)()(2)( nXnen

)(2

)()1( nnfnf

)()()()1( nXnenfnf

• µ is the step size

• µ must be determined in for the system to converge

• f

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)0(320XXrL

•

•

•

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)()()()1( nXnXnRnR TXXXX

)1()1()1( 1 nrnRnf dXXX

)()()()1( nXndnrnr dXdX

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∑

MATLAB/Simulink Xilinx System Generator

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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

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Least Mean Square◦ Design ◦ Test FIR filter structures◦ Implement

Hardware

Simulation MATLAB

◦ Least Mean Square (LMS)◦ Recursive Least Square (RLS)

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Speech Data Processing

MATLAB simulation with Tap (L) = 10◦ LMS◦ RLS

Speech Data

Recorded Voice Signal Recorded Engine Noise

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Figure 1: Desired Signal

Figure 2: Noise Signal

Figure 3: Reference Signal

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LMS RLS

Figure 4: LMS Filter Coefficients

Figure 5: RLS Filter Coefficients

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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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Description:• L = 6• Adaptive FIR Filter

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XtremeDSP- Virtex 4 Hardware Results

Orange – Input signalBlue – Output Signal

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Description:• L =10• Adaptive FIR Filter

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Figure 12: Desired Signal and Recovered

Signal

Figure 13: Spectrum of Desired and Recovered

Signals

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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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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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