Done By:
Samah Qissy
Sereen Essraj
Areej EL-Midana
Ghadeer Abu-safer
Submitted To:
Dr. Hatem El-Aydi
Contents.
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What is noise?
What is noise cancellation?
Simple idea.
Wave cancellation.
Active noise cancellation (ANC).
Applications.
Adaptive filter.
Comparison
Adaptive algorithm (LMS & RMS).
Simulation.
Conclusion.
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)
Fiq(1):AWGN
Active and passive noise
 The advantages of active noise control methods
compared to passive ones are that they are
generally:
• More effective at low frequencies.
• Less bulky.
• Able to block noise selectively
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
Cont….
 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)
Questions you may have about
such a device are:
 How can you cancel out simple waveforms?
 How is complex sound cancelled out?
 What are some noise cancellation applications?
Types of wave cancellation
wave cancellation
Simple wave
complex wave
Simple wave cancellation
Simple sine wave for single
sound frequency
Sum of two waves slightly
out of phase
One pure sound a fraction of
a second after the next
Sum of two waves
slightly out of phase
Canceling complex waves
A spoken word consists of a
spectrum of frequencies of
different amplitude
 need to filter each
frequency separately,
create the same frequency
and amplitude at 180° out of
phase.
Active Noise Cancellation (ANC)
Applications
 Headsets (headphone)
 Honda cars.
 Space satellite antennas.
 Use in apartment.
 .NoiseMuter
Noise Canceller( NoiseMuter)
NoiseMuter is an advanced noise suppression
solution, designed as add-on software module to
enable noise-free communication.
Condition/coder
Speech + Car
Noise
Original
NoiseMuter Processed
Original
NoiseMuter
processed
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
Output
signal
Input
signal
Filter
structure
Adaptive
algorithm
Criterion of
performance
The coefficients of an adaptive filter change
in time
Comparison
supervised vs. unsupervised
 Expected output is known.  Expected output is
unknown quantity.
quantity .
 the Criterion of
 The Criterion of
Performance has to do.
Performance can simply
We cannot simply take
take the difference
between actual output and
desired.
 It uses information to tell
 the difference actual and
the Adaptation Algorithm
desired output consists
what adjustments to make.
of looking for signal
qualities
Block diagram of adaptive system
Primary
signal
S(n)+No(n)
No(n)
d(n)
?
+
N1(n)
Reference
signal
adaptive
y(n)
output
e(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 algorithm
d(n)
 Estimates the
solution to the
X(n)
Weiner-Hopf
equations using
gradient descent
method which finds
minima by estimating
the gradient.
Y(n)
Transversal
Filter
e(n)
C(n)
LMS
• is the step size
e
Cont..
X(n)
Adaptive
filter
Unknown
system
 filtering operation with the
previous version of the coefficients.
 Compare the computed output
with the expected output.
Update the coefficients using
the following computation.
y(n)
d(n)
e(n)
Experiment
Click to listen original sound
Click to listen output1 sound
Click to listen output 2 sound
Click to listen nosily sound
click to show simulation graph1
Click to show simulation graph
Why using RLS algorithm
Increase performance .
Faster convergence behavior.
Infinite memory
Improve the final noise removal
performance.
Filter coefficient is much more stable .
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 RLS .