SIGNAL FILTERING HADJI Isma HAFNAOUI ImaneJune 2012 University of M’Hamed Bouguara - IGEE
OUTILINES Introduction Electronic filters 1D Signal Filtering The Butterworth filter The Wiener filter Conclusion
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.
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
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.
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.
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
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.
Butterworth Filter We will apply the low pass and high pass butterworth filter to the following noisy speech signal.
Butterworth Filter We design a 3rd order low-pass filter to suppress high frequencies and apply it to the previous noisy signal
Butterworth Filter We design a 3rd order high-pass filter to supress low frequencies and apply it to the previous noisy signal
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.
Wiener Filter We apply the wiener filter on the previous signal with the train noise and compare the results.
Wiener Filter The filtering effect is better observed in this audio recording with street/car background noise.
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.