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Band-pass filter – only frequencies in a frequency band are passed.High-pass filter – high frequencies are passed, low frequencies are attenuated.Low-pass filter – low frequencies are passed, high frequencies are attenuated.The frequency response can be classified into a number of different bandforms describing which frequency bands the filter passes (the passband) and which it rejects (the stopband):.Some terms used to describe and classify linear filters: m-derived filter, a modification of the constant k with improved cutoff steepness and impedance matching.Constant k filter, the original and simplest form of wave filter.Some important filters designed by this method are: Filters designed by this methodology are archaically called "wave filters". This results in each having a different transfer function.Īnother older, less-used methodology is the image parameter method.

The difference between these filter families is that they all use a different polynomial function to approximate to the ideal filter response.

Elliptic filter, has the steepest cutoff of any filter for a specified order and ripple.

Bessel filter, has a maximally flat phase delay.

Butterworth filter, has a maximally flat frequency response.

Chebyshev filter, has the best approximation to the ideal response of any filter for a specified order and ripple.

Some important filter families designed in this way are: The modern design methodology for linear continuous-time filters is called network synthesis. Any nonlinearity would potentially result in the output signal containing frequency components not present in the input signal. Circuits that perform this function are generally linear in their response, or at least approximately so. These circuits are generally designed to remove certain frequencies and allow others to pass. Linear continuous-time circuit is perhaps the most common meaning for filter in the signal processing world, and simply "filter" is often taken to be synonymous.

5.1 Filters for removing noise from data.

infinite impulse response (IIR) or finite impulse response (FIR) type of discrete-time or digital filter.

passive or active type of continuous-time filter.

discrete-time (sampled) or continuous-time.

Filters processing time-domain signals in real time must be causal, but not filters acting on spatial domain signals or deferred-time processing of time-domain signals.

causal or non-causal: A filter is non-causal if its present output depends on future input.

If the filter operates in a spatial domain then the characterization is space invariance. time-variant or time-invariant, also known as shift invariance.There are many different bases of classifying filters and these overlap in many different ways there is no simple hierarchical classification. Filters are widely used in electronics and telecommunication, in radio, television, audio recording, radar, control systems, music synthesis, image processing, and computer graphics. Correlations can be removed for certain frequency components and not for others without having to act in the frequency domain. However, filters do not exclusively act in the frequency domain especially in the field of image processing many other targets for filtering exist. Most often, this means removing some frequencies or frequency bands. Filtering is a class of signal processing, the defining feature of filters being the complete or partial suppression of some aspect of the signal. In signal processing, a filter is a device or process that removes some unwanted components or features from a signal. Device or process that removes unwanted components or features from a signal, or enhances wanted ones, or both