Design and implement an Laboratory Duct noise cancellation system using an adaptive filter

1. NOISE CONTROL IN
LABORATORY DUCT
USING ADAPTIVE FILTER
Submitted By: Rishikesh

2. GOAL:
•The goal of the project is to design and
implement an Laboratory Duct noise
cancellation system using an adaptive filter.

3. LABORATORY DUCT BLOCK DIAGRAM

4. ACTIVE NOISE CONTROL
• Active noise control (ANC) has received much attention in recent
years. In an ANC system, a secondary source is introduced to
generate anti-noise of equal amplitude but of opposite phase with
reference to the primary noise. ANC techniques can be utilized to
extract a signal buried in noise or to cancel unwanted noise.

5. WHAT IS NOISE ?
• Noise means any unwanted sound.
• Unwanted waveforms that can interfere with communication.

6. WHAT IS ACTIVE NOISE CANCELLATION ?
• Method for reducing unwanted sound by the addition of a
second sound specifically designed to cancel the first.
WHAT IS ADAPTIVE FILTER ?
• Adjust themselves to an ever-changing environment.
• Changes its parameters so its performance improves
through its surroundings.
WHY WE USE ADAPTIVE FILTER?
• Because some parameters of the desired processing operation are not
known in advance or are changing.

7. ADAPTIVE FILTERS
• A filter which adapts itself to the input signal given to it.
• It is non-Linear and Time Variant.
• 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.
Σ
N

y(n)  w n  x n 
k
k k
0
( ) ( )

8. ADAPTIVE ALGORITHM
• Least Mean Squares Algorithm (LMS) widely used Adaptive
algorithm for noise cancellation.
• The Least Mean Squares Algorithm (LMS) updates each
coefficient on a sample-by-sample basis based on the error
e(n).
w (n 1) ( ) ( ) ( ) k w n e n x n k k    
• μ (mu) is critical is Convergence Coefficient.
• μ is set by trail and error for each Application.

9. APPROACHES OF ANC
Feedforward
Topology
• Reference noise and
cancelled noise are used
• 2 inputs and 1 output
Feedback Topology
• Only cancelled noise are
used – one input and one
output

10. FEEDFORWARD TOPOLOGY
Estimatio
n of S(z),
Ŝ(z)
LMS
-
Secondary
Path , S(z)
x(n)
x^(n)
y(n)
e(n)
Duct system
DSP System
e(n)
Primary
function, P(z)
y’(n)
d(n)
W(z)
• Coherent input is captured,
filtered and feed into LMS
•Estimation of the secondary
path transfer function is
obtained by identification
process
-

11. FEEDFORWARD EXPERIMENTAL SETUP
Noise speaker
x(n) S(z)
Input noise
Canceling zone
Canceling speaker
Amplifier
microphone
Secondary Path
e(n)
y(n)
NI PXIe - 1071

12. FUTURE PLAN:
• Implement it on Hardware.
• Use Laboratory Duct model technique to design Adaptive
Active Noise Cancellation System.
• Active noise cancellation with a fuzzy adaptive filtered-X
algorithm.

13. APPLICATIONS
• Noise Cancellation Headsets (headphone)
• Bikes and cars.
• Space satellite antennas.
• Jet engines and heavy machinery.
• Noise-Muter and more..

14. REFERENCES:
• Adaptive recurrent fuzzy neural networks for active noise control
(http://www.sciencedirect.com/science/article/pii/S0022460X06
002628)
• Digital Signal Processing : Principles, Algorithms and
Applications 4e by Proakis and Manolakis.
• Signal Processing for Active Control by Stephen Elliott.
• Wikipedia.org
(http://en.wikipedia.org/wiki/Active_noise_control).
• http://www.analog.com/library/analogDialogue/archives/34-
02/noise/ .
• http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=
6187929&queryText%3DActive+noise+control.

15. THANK YOU !