This document discusses signal filtering techniques. It introduces electronic filters and their characteristics such as cut-off frequency, stop band, and pass band. It describes different types of filters and provides examples of their applications. The document then focuses on 1D signal filtering, specifically analyzing audio signals. It explains the Butterworth filter and Wiener filter, applying each to a noisy speech signal to suppress frequencies and reduce noise. The Wiener filter is found to have better noise reduction capabilities. In conclusion, signal filtering is important for signal processing, and the Butterworth and Wiener filters can be used to filter 1D noisy signals.

1. SIGNAL
FILTERING
HADJI Isma
HAFNAOUI Imane
June 2012 University of M’Hamed Bouguara - IGEE

2. OUTILINES
 Introduction
 Electronic filters
 1D Signal Filtering
 The Butterworth filter
 The Wiener filter
 Conclusion

3. Introduction
 Most of the signals we deal with in real life get
corrupteed in some way or another by some
unwanted signals.
 For the purpose of signal processing and
analysis, it is imperative to get rid of these
interferences, or at least reduce their effects.
 This is achieved through applying Signal
Filtering techniques.

4. Electronic Filters
 A Filter is an electronic circuit that removes /
attenuates, from a signal, some unwanted
component or feature.
 Filter Application
 Eliminate background noise
 Radio tuning to a specific frequency
 Direct particular frequencies to different speakers
 Modify digital images
 Remove specific frequencies in data analysis

5. Filter Characteristics
 To understand the basics of filtering, it is first
necessary to learn some important terms used to
define filter characteristics.
 Cut-Off Frequency (fc): Also referred to as the corner
frequency, this is the frequency or frequencies that define
the limits of the filter range.
 Stop Band: The range of frequencies that is filtered out.
 Pass Band: The range of frequencies which is let through
and recorded.
 Transition Band: region that
Separates the passband and
stopband.

6. Types of Filters
 Electronic filters can be:
 passive or active
 analog or digital
 high-pass, low-pass, bandpass, band-reject or all-
pass.
 discrete-time (sampled) or continuous-time
 Linear or non-linear
 infinite impulse response (IIR type) or finite impulse
response (FIR type)
 Most commercial filters use analog technology. In
other words, these instruments porcess signals
(speech or music) in a continuous (analog) pattern
as they exist in the environment.

7. 1D Filtering
 1 D signals can be voltage or current recordings, ecg
or eeg signals or else audio signals.
 Speech signal is one of the most important signals in
multimedia applications.
 Speech signals degrade due to the presence of noise
and therefore noise reduction is an important field of
speech processing.
 We will examine audio filtering in the sense of specific
frequency suppression and extraction.
 There are many different types of filters available for
the construction of filters. We will specifically use the
Butterworth filter and the wiener filter and compare
their action

8. Butterworth Filter
 A Butterworth filter is a signal processing filter
that has an extremely flat frequency response
in the passband. It is referred to as a maximally
flat magnitude filter and is commonly used in
both analog and digital audio filters.

9. Butterworth Filter
 We will apply the low pass and high pass
butterworth filter to the following noisy speech
signal.

10. Butterworth Filter
 We design a 3rd order low-pass filter to
suppress high frequencies and apply it to the
previous noisy signal

11. Butterworth Filter
 We design a 3rd order high-pass filter to
supress low frequencies and apply it to the
previous noisy signal

12. Wiener Filter
 the Wiener filter is a filter proposed by Norbert
Wiener during the 1940s
 Its purpose is to reduce the amount
of noise present in a signal by comparison with
an estimation of the desired noiseless signal.

13. Wiener Filter
 We apply the wiener filter on the previous signal with the
train noise and compare the results.

14. Wiener Filter
 The filtering effect is better observed in this audio
recording with street/car background noise.

15. Conclusion
 In this presentation, the concept of signal
filtering was presented where the importance
of filters in signal processing was brought to
the forefront.
 The butterworth and Wiener Filters were
tested on filtering samples of 1D noisy signals.
 Where Butterworth was able to filter the noise
to a certain degree, Wiener is observed to
have a better noise reduction.

16. THANK YOU