jessica arbona & christopher brady dr. in soo ahn & dr. yufeng lu, advisors
TRANSCRIPT
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(3
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XXrL
•
•
•
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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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