Signal processors utilize filters to eliminate undesired elements of a signal, such as random noise, or to extract valuable parts of a signal, such as portions of the signal that fall within a certain range.
Using this method, an audio recording made using inferior equipment may be filtered to eliminate the undesirable parts from the signal, resulting in a recording that sounds as similar to the original audio as possible (without interruptions).
Generally speaking, digital filters may be divided into two categories: FIR (finite-duration impulse response) filters and IIR (infinite-duration impulse response) filters. It is known as the impulse response of a system when a system receives some kind of input and the result or reaction that is obtained is known as the response.
FIR Vs. IIR filters
The distinction between FIR and IIR filters is that the former’s impulse response is nonzero for only a limited number of samples, whilst the latter’s is nonzero for an infinite number of samples. IIR filters are capable of processing an endless number of nonzero samples. IIR filters are sometimes referred to as feedback filters, but FIR filters do not have a feedback mechanism of any kind. It is possible to have feedback components in the filter coefficients of IIR filters while solving a differential equation.
What exactly are FIR filters?
FIR filters are digital filters that create a limited impulse response of a dynamic system. FIR filters are also known as finite impulse response filters. The impulse response given by FIR filters has a limited duration because of the nature of the filter. This is due to the fact that the response supplied by these filters is fixed to zero after a certain amount of time has passed. As an example, consider FIR filters. The nth-order filter creates (n+1) samples before being set to zero.
FIR filters do not have a feedback mechanism like other types of filters. Their current input contains only the values from the current and previous inputs. The output of FIR filters is formed by summing a finite number of finite samples of input values, which is a finite number of samples of input values. FIR filters are better suitable for applications that demand a linear phase response, such as those in the automotive industry.
What exactly are IIR filters?
IIR filters are digital filters that produce an infinite impulse response of a dynamic system. IIR filters are also known as infinite impulse response filters. This input is composed of the current and previous inputs in combination with the past outputs and is referred to as the present input.
The IIR filter acts in such a manner that not only the current and previous input samples but also the previous output sample are taken into account. This feedback circuitry is what distinguishes them from FIR filters in terms of performance.
The intrinsic feedback mechanism of these filters causes them to be recursive in the natural world. They never allow their reaction to a given stimulus to reach a state of equilibrium. They are more computationally efficient and consume less memory than their predecessors. They are, however, less stable and more difficult to govern as a result of their recursive nature.
IR filters are ideally suited for situations in which no phase information is required, such as signal amplitude monitoring, when no phase information is required.
Difference between FIR filters and IIR filters
- In nature, FIR filters are non-recursive in design. Because they feature a feedback mechanism, IIR filters have a cyclical aspect in nature. The latter makes use of a feedback system in which the prior output, when combined with the current and past input, is presented as the present input, and vice versa.
- FIR filters are less computationally efficient than IIR filters, despite the fact that they are simpler to build. Because of the existence of a feedback loop, it is difficult to include IIR filters into a circuit design.
- FIR filters have a longer reaction time than other types of filters. IIR filters have a shorter reaction time than other types of filters.
- When compared to IIR filters, FIR filters need the use of greater memory. Because they are not recursive, FIR filters are also more stable than other types of filters. Because IIR filters are recursive, they are inherently unstable.
- FIR filters are less sensitive and simpler to manage than IIR filters, which makes them a better choice for certain applications.
Conclusion
Digital filters may be used for a variety of different applications. For example, a filter may be characterized as either a high pass, low pass, band stop, or bandpass depending on its performance characteristics. A low pass filter, for example, may be used to eliminate high-frequency noise from an input signal, resulting in a signal that is free of high-frequency noise.
These filter types may be implemented using either FIR or IIR filters, depending on the application. Additionally, a combination of these two techniques may be utilized to create an arbitrarily shaped filter. There is no feedback mechanism in FIR filters. As a result, they become more stable. They are employed in situations where linear phases are required.
IIR filters employ the prior outputs, as well as the current and previous inputs, as feedback for their operation. As a result, they become recursive and less stable in the natural world. The use of IIR filters, as opposed to FIR filters, may provide the necessary filtering characteristic with fewer memory and computations.