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                                               MEng Final Year Project                                                                                 0
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 DSP Techniques for Active Noise                                                     David Walker
 Control                                                                             Course Code: EEM4, Student ID No. 337862

                                                                                     Supervisor: Dr B. Vuksanovic and Mr V. Dunn
                                                                                     Location: Anglesea A0.07

                                                                           Introduction
Active Noise Control (ANC) is the electro-acoustic generation of sound to reduce the volume of noise. The main application of ANC is reduction of
lower frequency, periodic noise, such as that generated from a fan.
Adaptive filtering is used to model the acoustic paths with the system coefficients updated by the Filtered-X Least Mean Squares algorithm.
A major design consideration is the number of coefficients used in the filter versus the execution speed.

Project Overview                                                                  Work Completed
• An investigation into ANC and the FX-LMS algorithm using Matlab                 • Completed Matalb simulation
                                                                                  • C Prototype algorithm complete, results shown below
• Convert and prototype algorithm in C using Visual Studio for real-time
processing                                                                        • Microphone preamp, switched capacitor anti-aliasing filters, 5th order
                                                                                  reconstruction filter and power amplifiers built
• Build system hardware
                                                                                  • Real-time speaker path estimation
• Modify algorithm for Sharc AD21161n Digital Signal Processor                    • Proposed modifications: Decimation with Window-Sinc filter,
                                                                                  Upsamping with linear interpolation, Modification to circular buffering
• Analyse speed of execution and success of system
                                                                                  technique
• Propose improvements to system and algorithm

System Block Diagram
The Diagram below illustrates a feed-forward ANC system. The noise is measured at source by the Input Microphone x(n) and is sampled by the
controller. The noise then passes through a system S(z) (such as a room) and is altered. This altered noise is measured by the Error Microphone e
(n) and is also fed into the controller. The aim of the controller is to produce a signal y(n) which minimises the error (or noise).


                                               d(n)                              FX-LMS update process
                                 Unknown
                                 System W(z)                    e(n)=d(n)+s(n)   The sound contributing to cancelation can be defined as
   x(n)                                                                          (*denotes convolution):
                                                                                 s(k) = w(k) * (x(k) * c(k))
                                               s(n)
                                                                                 x is the noise reference, w is the filter coefficients and c is the
                                                                                 cancelation path. This defines that x must be filtered by c which
                         W’(z)
                  f(n)                                                           gives the filtered-X algorithm.
                                                  Unknown
          C’(z)                                   System C(z)                    The c coefficients are estimated offline to produce the estimate
                                                                                 c’(k), and the w coefficients are updated by:
                                                                                 w(n+1) = w(n) – 2.mu.e(n).f(n)
                                                                                 Where f(n) = x(n)*c’(k) and e(n) is the residual noise.




Department of Electronic and Computer Engineering

				
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