Jessica Arbona & Christopher Brady
Dr. In Soo Ahn & Dr. Yufeng Lu, Advisors
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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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Adaptive System Identification
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Adaptive Noise Cancellation
d(n)
Noise
Delay
x(n)
FIR
Adaptive
Filter
y(n)
Adaptive
Filter
Algorithm
Adaptive Prediction
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e(n)
Adaptive Inverse
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Cost Function
J  E{e2 (n)}
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Wiener-Hopf equation
◦ Df opt
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1
 RXX  rdX
Least Mean Square (LMS)
Recursive Least Square (RLS)
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Widrow-Hoff LMS Algorithm
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◦ f ( n  1)  f ( n)  ( n)
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◦ (n)  2  e(n)  X (n)
f (n  1) 
◦ d
f (n)    e(n)  X (n)
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µ is the step size
µ must be determined in for the system to
converge
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f0   
3  L  rXX (0)
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• RXX (n  1)
• rdX (n  1)
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 RXX (n)  X (n)  X T (n)
 rdX (n)  d (n)  X (n)
1
f (n 1)  RXX (n 1)  rdX (n 1)
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Design Tools
MATLAB/Simulink
Xilinx System Generator
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Xtreme DSP development kit:
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Hardware
FPGA device (Virtex4 xC4SX35-10FF668)
Two 14- bit DAC onboard channels
Ultrasound Data
SignalWave DSP/FPGA board
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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 (XC2V300FF1152)
Speech Data
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Simulation
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MATLAB
◦ Least Mean Square (LMS)
◦ Recursive Least Square (RLS)
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Hardware
Least Mean Square
◦ Design
◦ Test FIR filter structures
◦ Implement
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Speech Data Processing
MATLAB simulation with Tap (L) = 10
◦ LMS
◦ RLS
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Speech Data
Recorded Voice Signal
Recorded Engine Noise
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Figure 1: Desired Signal
Figure 3: Reference Signal
Figure 2: Noise Signal
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LMS
Figure 4: LMS Filter
Coefficients
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RLS
Figure 5: RLS Filter
Coefficients
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LMS
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Figure 8: Desired Signal
and Recovered Signal
RLS
Figure 9: Desired Signal and
Recovered Signal
Green – Desired Signal
 Blue – Recovered Signal
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System Components
Xilinx Blocks
ROM Block
Multiplexer (2x)
Adaptive Filter
Xtreme DSP Block
DAC Block (2x)
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Description:
•L = 6
• Adaptive FIR Filter
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XtremeDSP- Virtex 4
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Hardware Results
Orange – Input signal
Blue – Output Signal
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System Components
Xilinx Blocks
ROM Block
Adaptive Filter
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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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