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Active Noise Cancellation System
Students:Jessica Arbona & Christopher Brady
Advisors:Dr. Yufeng Lu
Outline
Goal Adaptive Filters
What is an adaptive filter? Four Typical Application of Adaptive Filter How Adaptive Filters works
Ultrasound Data Data Collection Filter Results
Speech Data Filter Simulation
Summary Future Plans
Goal
The goal of the project is to design and implement an active noise cancellation
system using an adaptive filter.
What is an Adaptive Filter?
An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by
an error signal.
Four Typical Applications of Adaptive Filter
Adaptive System Identification Adaptive Noise Cancellation
Adaptive Prediction Adaptive Inverse
How Adaptive Filters Works
Cost Function
Wiener-Hopf equation
Least Mean Square (LMS) Recursive Least Square (RLS)
dXXXopt rRf 1
)}({ 2 neEJ
LMS implementation
Widrow-Hoff LMS Algorithm
)()(2)( nXnen
)(2
)()1( nnfnf
)()()()1( nXnenfnf
Convergence of LMS
)0(3
20
XXrL
RLS implementation
)()()()1( nXnXnRnR TXXXX
)1()1()1( 1 nrnRnf dXXX
)()()()1( nXndnrnr dXdX
Ultrasound Data Processing
Ultrasonic Measurement System
Hardware
Variable.m
Xilinx’s block- ROM
Loading the Variables
Hardware Design without Adaptive Filter
Preliminary Results
Hardware Simulation Software Simulation
Hardware SimulationXtremeDSP- Virtex 4
Preliminary Results
Hardware Design with Adaptive Filter
Hardware Design of the Adaptive Filter
Tap
XtremeDSP Development Kit – Virtex-4 Edition
Key Features:•Xilinx Devices•Two Independent DAC Channels•Support for external clock, on board oscillator
Progressive Results of the Input Signal [x] & Output Signal
[y]XtremeDSP- Virtex 4 Simulation
Speech Data Processing
MATLAB simulation with L = 10 LMS RLS
MATLAB simulation with L = 7 RLS
Speech Data
Recorded Voice SignalRecorded Engine Noise
05.0
10L
kHzf s 5.22
Noise and Desired signal
Figure 1: Desired Signal
Figure 2: Noise Signal
Figure 3: Reference Signal
Spectral Analysis of Noise and Desired
Figure 4: Spectrum of Desired Signal
Figure 5: Spectrum of Noise Signal
Figure 6: Spectrum of Reference Signal
LMS filter coefficients
Desired and Recovered signal from LMS
Figure 7: Desired Signal and Recovered Signal
Figure 8: Spectrum of Desired and Recovered Signals
RLS Filter Coefficients with L = 10
Desired and Recovered signal from RLS
with L = 10
Figure 9: Desired Signal and Recovered Signal
Figure 10: Spectrum of Desired and Recovered Signals
RLS Filter Coefficients with L = 7
Desired and Recovered from RLS withL = 7
Figure 11: Desired Signal and Recovered Signal
Figure 12: Spectrum of Desired and Recovered Signals
Summary
To Be complete How mu changes the
system performance Comparison of
Different FIR filter structure
Implement on SignalWave board
Hardware calculation for mu value
RLS hardware implementation
Completed Speech data
simulation LMS RLS
LMS hardware implementation.
ScheduleFall Schedule
Date Milestone
Jessica Christopher
Thursday, November 17 Different FIR Form / Proposal Work on Mu value / Proposal
Thursday, December 1 Different FIR Form Work on Mu value
Spring Schedule
Date Milestone
Jessica Christopher
Thursday, January 19 Signal Wave Board Research on Acoustic Noise Suppression
Thursday, January 26
Thursday, February 2 Hardware Calculation for Mu Design and Simulate Noise Suppression System
Thursday, February 9
Thursday, February 16 RLS hardware Design with Matrix Inversion
Thursday, February 23 Testing of Noise Suppression System
Thursday, March 1
Thursday, March 8 Implementation Noise Suppression System
Thursday, March 22
Thursday, March 29
Thursday, April 5
Thursday, April 12 Preparing for Final Report
Thursday, April 19
Thursday, April 26
Reference
[1] D. Monroe, I. S. Ahn, and Y. Lu, “Adaptive filtering and target detection for ultrasonic backscattered signal”, IEEE International Conference on Electro/Information Technology, May 20-22, 2010, Normal, Illinois.
QUESTIONS?
