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)

LMS implementation Widrow-Hoff LMS Algorithm

Convergence of LMS  

RLS implementation

Ultrasound Data Processing Ultrasonic Measurement System

Hardware Upload the Variables to the Design Loading the Save Workspace

Variable.m

Xilinx’s block- ROM

Loading the Variables

Hardware Design without Adaptive Filter

Preliminary Results Hardware Simulation Software Simulation

Preliminary Results XtremeDSP- Virtex 4 Hardware Simulation X Signal Y Signal

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

Speech Data Recorded Voice Signal Recorded Engine Noise

Noise and Desired signal Figure 1: Desired Signal Figure 3: Reference Signal Figure 2: Noise Signal

Spectral Analysis of Noise and Desired Figure 4: Spectrum of Desired Signal Figure 6: Spectrum of Reference Signal Figure 5: Spectrum of Noise 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 with L = 7 Figure 11: Desired Signal and Recovered Signal Figure 12: Spectrum of Desired and Recovered Signals

Summary Completed To Be complete Speech data simulation LMS RLS LMS hardware implementation. 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

Schedule Fall Schedule Date Milestone Jessica Christopher   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 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?