NOISE CONTROL IN LABORATORY DUCT USING ADAPTIVE FILTER

Submitted By: Rishikesh

GOAL:

•The goal of the project is to design and

implement an Laboratory Duct noise

cancellation system using an adaptive filter.

LABORATORY DUCT BLOCK DIAGRAM

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.

WHAT IS NOISE ?• Noise means any unwanted sound.

• Unwanted waveforms that can interfere with communication.

• Method for reducing unwanted sound by the addition of a

second sound specifically designed to cancel the first.

WHAT IS ACTIVE NOISE CANCELLATION ?

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.

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.

∑

N

k

k knxnw y(n)0

)()(

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).

• µ (mu) is critical is Convergence Coefficient.

• µ is set by trail and error for each Application.

)()()( 1)(nw k nxnenw kk

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

FEEDFORWARD TOPOLOGY

Estimation 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

-

FEEDFORWARD EXPERIMENTAL SETUP

Noise speaker

Input noiseCanceling speaker

Canceling zone

Amplifier

microphone

Secondary Path

e(n)

y(n)

S(z)x(n)

NI PXIe - 1071

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.

APPLICATIONS

• Noise Cancellation Headsets (headphone)

• Bikes and cars.

• Space satellite antennas.

• Jet engines and heavy machinery.

• Noise-Muter and more..

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.

THANK YOU !