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Done By S . Malki Hussain S.Chand Basha S . Md .Javeed B.Hussain Basha S.Baba Fakruddin S.Minuddin Submitted to S . Fouziya Parveen

Low power vlsi implementation adaptive noise cancellor based on least means square algorithm

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Done By

S . Malki Hussain S.Chand BashaS . Md .Javeed B.Hussain Basha

S.Baba Fakruddin S.Minuddin

Submitted toS . Fouziya Parveen

Goal What is noise ?What is Noise Cancellation ? Simple Idea . Applications Adaptive Filter Adaptive Algorithm( LMS ) Simulation Conclusion

Goal

The goal of the project is for

.

Equipment Lists

Design Tools

MATLAB/Simulink

Xilinx System Generator

Design Approach

Simulation

MATLAB

. Least Mean Square (LMS)

Xilinx

. Lease Mean Square (LMS)

What is noise? Noise consists of unwanted waveforms that can interfere

with communication.

Sound noise: interferes with your normal hearing

.Loud noises

.Subtle noise

.White noise (AWGN)

What is Noise Cancellation?

Noise cancellation is a method to reduce or completely cancel out undesirable sound.

call Active Noise Cancellation .

Noise cancellation tries to 'block' the sound at the source instead of trying to prevent the sounds from entering our ear canals .

These technologies are in their early stages.

The hope is that one day that these technologies can be used to minimize all sorts of unwanted sounds around us

Simple Idea

Cancellation processes depend on simple principle

adding two signals with the same

amplitude and opposite phase the result will be zero signals.

(H)

Simple wave cancellation

Applications

Headsets (headphone)Honda cars.Space satellite antennas. Use in apartment. Noise Muter

Adaptive Noise Cancelling Adaptive noise cancelling

- An approach to reduce noise based on reference noise signals

- System output

- The LMS algorithm

K

kktnkwtntstu

1 10 )()()()()(

)()()( 1 ktntukw

Adaptive filter

nonlinear and time-variant .

adjust themselves to an ever-changing environment .

changes its parameters so its performance improves through its surroundings.

Adaptive Filter

Outputsignal

Inputsignal

Adaptive algorithm

Criterion of performance

Filter structure

The coefficients of an adaptive filter change in time

Block diagram of adaptive system

?

Primary

signal

d(n)

N1(n)

Reference

signaly(n)

output

e(n)

adaptive

No(n) S(n)+No(n)

Adaptive algorithm

An adaptive algorithm is used to estimate a time varying signal.

By adjusting the filter coefficients so as to minimize the error.

There are many adaptive algorithms like Recursive Least Square (RLS),Kalman filter,

but the most commonly used is the Least Mean Square (LMS) algorithm.

LMS Adaptive Algorithm

Introduced by Widrow & Hoff in 1959.

Simple, no matrices calculation involved in the adaptation.

In the family of stochastic gradient algorithms.

Approximation of the steepest – descent method

Based on the MMSE criterion.(Minimum Mean square Error)

Adaptive process containing two input signals:

• 1.) Filtering process, producing output signal.

• 2.) Desired signal (Training sequence)

Stability of LMS

The LMS algorithm is convergent in the mean square if and only if the step-size parameter satisfy

Here max is the largest eigenvalue of the correlation matrix of the input data

More practical test for stability is

LMS Algorithm Steps

Filter output

Estimated error

1

0

*M

k

k nwknuny

nyndne

The LMS Equation

The Least Mean Squares Algorithm (LMS) updates each coefficient on a sample-by-sample basis based on the error e(n).

This equation minimises the power in the error e(n).

The value of µ (mu) is critical.

If µ is too small, the filter reacts slowly.

If µ is too large, the filter resolution is poor.

The selected value of µ is a compromise.

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

LMS algorithm Estimates the solution to the Widrow -Hoffequations using gradient descent method which Finds minima by estimatingthe gradient.

X(n)

C(n)

Transversal Filter

LMS

Y(n)

e(n)

d(n)

is the step size

Cont..

e(n)

Adaptive

filter

Unknown

system

X(n)

d(n)

y(n)

filtering operation with theprevious version of the coefficients.

Compare the computed output with the expected output.

Update the coefficients usingthe following computation.

Cont..LMS algorithm

The most widely used real time adaptive filtering algorithm

Convergence speed of the LMS algorithm

Controlled by the spread of eigenvalues of the autocorrelation matrix of the input data

Enhanced by reducing the eigenvalue spread

Advantages

low computational complexity

simple to implement

allow real-time operation

SimulationXilinx System Generator Output

Conclusion

Active noise cancellation is a method to cancel out undesirable sound in real time

The adaptive filter is used to estimate the error in noisy wave

Many algorithms are used in adaptive filter like LMS RLS & MSE and the better is LMS .