Enhancement of Speech in Noisy
Conditions
Project Presentation
Paul Coffey
Contents
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
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Project Overview
System Development
Testing
Conclusion
Paul Coffey
Project Presentation
Project Overview
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This project is about The “Enhancement of
Speech in Noisy Conditions”
Speech Enhancement is used in communications
everywhere
–
From Mobile Phones to Speech Recognition Systems
Paul Coffey
Project Presentation
Project Overview

To Investigate Spectral
Subtraction
–
–
A method to improve the
quality of a signal that has
been effected by noise
This is done by taking noise
from degraded signal
Noisy Signal
Analysis Window
Fourier Transform
Spectral Modification
Inverse Fourier Transform
Synthesis Window
X ( f )  S ( f )  P( f )
Overlap Add
Enhanced Signal
Paul Coffey
Project Presentation
Project Overview

Implement Wiener Filter
Method
–
Noisy Signal
Is derived and Works on
same basis as Spectral
Subtraction
Speech and noise power
spectrum estimation
PXX(f)
PNN(f)
Wiener Filtering

Pxx  f 
W  f   
  Pxx  f   Pyy  f


 
Paul Coffey
Wf

Pxx  f



  Pxx  f   Pyy  f  
  
Enhanced Signal
Project Presentation
System Development
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
Paul Coffey
4
4
Original Speech Sample
x 10
2
Mag
To begin development, a
simple task such as
doubling the magnitude
was implemented
Also getting the system to
analyse and synthesise
the whole signal
Hamming Windows and
Overlap Add were also
introduced to the system
0
-2
-4
0
500
1000
1500
500
1000
1500
5
1
x 10
2000
2500
Samples
New Speech signal
3000
3500
4000
3000
3500
4000
0.5
Mag

0
-0.5
-1
0
2000
Samples
2500
Project Presentation
System Development
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Finally a basic Spectral
Subtraction
implementation of the
filter was then achieved
Result from running the
filter is shown
MagY = MagX – MagNN;
Original , Noisy and Filtered speech
1
0.5
0
Mag

-0.5
-1
-1.5
0
0.5
1
1.5
2
Time
Paul Coffey
Project Presentation
2.5
4
x 10
System Development
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The other filter that was developed was the
Wiener Filter
This was approached in the same way as the
previous filter
Since this method is similar to the way it is
derived as Spectral Subtraction, this greatly
helped with speeding up development
Paul Coffey
Project Presentation
System Development
Basic Wiener Filter
Implementation
 Result of the signal being
filtered shown
 This is implemented using
the code below:
W = MagX./(MagX+MagNN);
MagY = MagX .* W;

Paul Coffey
Original, Noisy and Filtered Speech Signal
1
0.5
Mag
0
-0.5
-1
-1.5
0
0.5
1
1.5
Time
2
2.5
Project Presentation
3
4
x 10
System Development
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Noise Detection and SNR were also needed to be
developed
These were required for the filters to operate properly
Finding the energy levels of the signal was used to
implement the noise detection method
The code to do so is: E = sum (x.^2);
The SNR is then was developed using the equation
SNR db  10 log
Paul Coffey

10

2
x
2
n
Project Presentation
Testing

Throughout the project tests were being carried
out to see how the filters were working
Spectral
Subtraction
here with SNR
of 10db
Original, Noisy and Filtered with SNR of 10db
0.8
0.6
0.4
0.2
0
Mag

-0.2
-0.4
-0.6
-0.8
-1
-1.2
0
0.5
1
1.5
Samples
Paul Coffey
2
2.5
4
x 10
Project Presentation
Testing
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Here is the Wiener Filter
Implementation with SNR
at 10db
It is the same speech
signal as used in the
Spectral Subtraction
implementation
Can see Wiener doesn’t
cut out speech like the
Spectral did
Original, Noisy and Filtered signal with SNR at 10db
0.8
0.6
0.4
0.2
0
Mag

-0.2
-0.4
-0.6
-0.8
-1
-1.2
0
0.5
1
1.5
2
Samples
Paul Coffey
Project Presentation
2.5
4
x 10
Testing
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A blind test was carried out involving ten student
in the Electronics Department
Consisted of number of different samples at
different levels of SNR
Results from this test showed at low levels of
SNR the two filters are very close
Then at higher levels of SNR the Wiener Filter is
preferred
Paul Coffey
Project Presentation
Conclusion
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Speech Enhancement techniques are a very
important part of Communication Systems
The two techniques, Spectral Subtraction and the
Wiener Filter were implemented
From the testing the Wiener Filter was the
preferred choice over the Spectral Subtraction
Paul Coffey
Project Presentation
Questions?
Paul Coffey
Project Presentation