Using Mean Filter And Show How The Window Size Of The Filter Affects Filtering The document discusses mean filtering and how the window size affects filtering. It defines mean filtering as replacing the center value in a window with the average of all values. A larger window size results in more smoothing as the average is taken over more points. The document provides examples of mean filtering a 3x3 window and pseudocode for a mean filter with a window size of 5. It also discusses edge effects, functions, sampling, filtering, noise addition, and signal observations at different points in the process.

1. Using Mean Filter And Show How The
Window Size Of The Filter Affects Filtering
TASIRA ADNEEN (10.01.05.041)
SATYAJIT NAG(10.01.05.042)
FERDAUS ALAM(10.01.05.044)
FAHIMUL ISLAM(10.01.05.045)
MD. ABDULLAH ALL MAMUN(10.01.05.047)

2. OUTLINES
• Definition
• Basic Concept about Mean Filter
• Algorithm
• Function
• Code operation overview
• Comparisons with other filtering
• Final filtered output concepts
• Advantages
• Disadvantages
• Utilizations
• Summary

3. Audio Filter Conception
• Working in the certain audio frequency range.
• frequency dependent amplifier
• Noise reduction is the process of removing noise
from a signal

4. Mean Filter for window size of the
filter affects
• Mean filter, or average filter is windowed filter of linear
class, that smoothes signal .The filter works as low-pass
one
• Replaces the center value in the window with the average.
• filter is for any element of the signal take an average across
its neighborhood.

5. Example
• Mean filtering of a single 3x3 window of values
• Center value is replaced by the mean of all nine values

6. Concepts
• Simple Averaging Formulation.sum up elements and divide
the sum by the number of elements.
• Filtered 1D signal by mean filter-

7. Algorithm
• Place a window over element
• Take an average
PSEUDO CODE FOR MEAN FILTER WITH WINDOW OF SIZE 5 :
** Move window through all elements of the signal**
for (int i = 2; i < N - 2; ++i)
**Take the average_result[i - 2] =
(signal[i - 2]+ signal[i - 1]+ signal[i]+signal[i + 1]+signal[i + 2])/ 5)
signal  input signal
result  output signal
N  length of the signal
for i = (N-1): length(yn)-(N-2)
x(i)= (yn(i-(N/2))+yn(i) + yn(i+(N/2)))/N
yn = q' + 1*randn(1,length(q))  adding noise

8. Edge Treating
• Window filters there is some problem of edge
treating.
• signal should be extended.

9. Functions
• Noise reduction process
• read a audio signal before filtering it for noise using
wavread function
• record a audio as a wav format in range of 1 min
• reading a signal named speech_dft.wan
• Sampling
• Filtering
• Noise adding
• Denoising

10. Sampling
• Sample the signal at different frequencies.
• Sampling in high frequencies results a better signal.
• Filter function and better algorithm for less use of
memory and time.

11. Filtering and Denoising Process
• Select the portion of the signal
• Determining the range of the signal for filtering
• More points need a better filtering function
• Adding noise
• Filtering with using Filter Function
• Technique used for signal denoising

12. Signal Observations At Different
Point of Operation

13. Comparison Of Mean Filter With
other Audio filters
 Comparing with Median Filter
 Order filters difference. mean filter
smooth out local variations within an
audio Signal.
median filter selects the
middle value from the original set
of recorded signal and mean filters
function by finding some form of
an average within window based
filtering.
 Maximum and minimum filters are
two order filters that ,using for
elimination noise.

14. Comparison Of Mean Filter With other
Audio filters
• Comparing With Gaussian Filter
geometric mean filter works
best with gaussian noise and
retains detail information
better .
In Gaussian Noise, each signal
will be changed from its original
value by a small amount
a smoothing mean filter sets each
sample of average value, or a
weighted average, of itself and its
nearby neighbors. And the Gaussian
filter is just one possible set of weights.
Gaussian filter is similar to mean filter. The
difference between them is that in mean filter,
every neighbor has the same contribution to
the final value.

15. Comparison Of Mean Filter With other
Audio filters
• Comparing With Moving Average filter
A moving average filter smoothes data by
replacing each data point with the average
of the neighboring data points defined
within the span which’s quite similar to
mean filter function
The span must be odd, in case of moving
average filter and The span can be even or odd in mean
filter.
The data point to be smoothed must be
at the center of the span
For example, To smooth data using a moving average filter with a span of 5.
ys(1) = y(1)
ys(2) = (y(1)+y(2)+y(3))/3
ys(3) = (y(1)+y(2)+y(3)+y(4)+y(5))/5
ys(4) = (y(2)+y(3)+y(4)+y(5)+y(6))/5

16. Advantages of Mean Filter
• Mean filtering is a non-linear filtering technique which is sometimes
useful as it can preserve sharp features in an Audio signal filtering
noise.
• The most basic of this filter operation is the arithmetic mean filtering
which finds the arithmetic average for corresponding reduction of
noise from audio input.
• Fault detection analysis is possible to improve by noise elimination.
• Mean filtering is a simple, intuitive and easy to implement method of
smoothing images.

17. Disadvantages of Mean Filter
• Low frequency information in the background has
not been affected significantly by filtering.
• the mean filter would cope with Gaussian noise
which was not symmetric about zero.
• It is difficult to treat analytically the effect of a
median filter. There is no error propagation.

18. Utilizations
• Mean filtering is most commonly used as a simple
method for reducing in a signal.
• noise filtering provide more accurate detection and
localization of defects on distorted image .
• It is used as an edge detector on the image
processing.

19. Final Output Observations
• Through this whole process we produced an
audio signal and took a portion of it and
added noise with it. Then we did
filtering(Mean).Then we saw the final output
of this filtering process .We se that this
output is with huge amount of noise. That is
our system error. We can represent this
error by a plotting a curve.

20. Error curve

21. • The effect of this filter on an audio signal is that
the audio signal is reduced in strength.
However, the audio signal still remain clearly
audible. But also the `clear' audio signal is
affected. With filter lengths greater than 5, the
quality of the output audio signal degrades
rapidly. This filter behaves like a
crude(raw/natural) low pass filter.

22. THANK YOU