FIR Filter
Updated: April 12, 2016
Creates a finite impulse response filter for signal processing
Category: Data Transformation / Filter
You can use the FIR Filter module to define a kind of filter called a finite impulse response (FIR) filter. FIR filters have many applications in signal processing, and are most commonly used in applications that require a linear-phase response.
After you have defined a filter that meets your needs, you can apply the filter to data by connecting a dataset and the filter to the Apply Filter module.
For Order, type an integer value that defines the number of active elements used to affect the filter's response. The order of the filter represents the length of the filter window.
For Window, select the shape of the data to which the filter will be applied. Azure Machine Learning supports the following types of windowing functions for use in finite impulse response filters:
- Hamming
The generalized Hamming window provides a type of weighted averaging, which is commonly used in image processing and computer vision.
- Blackman
A Blackman window applies a smoothly tapered curve function to the signal. The Blackman window has better stopband attenuation than other window types.
- Rectangular
A rectangular window applies a consistent value inside the specified interval and applies no value elsewhere. The simplest rectangular window might replace n values in a data sequence with zeros, which makes it appear as though the signal suddenly turns on and off.
A rectangular window is also known as a boxcar or Dirichlet window.
- Triangular
A triangular window applies filter coefficients in a step-wise fashion. The current value appears at the peak of the triangle, and then it declines with preceding or following values.
- None
In some applications it is preferable not to use any windowing functions. For example, if the signal you are analyzing already represents a window or burst, applying a window function could deteriorate the signal-to-noise ratio.
For Filter type, select an option that defines how the filter will affect values. You can specify that the filter exclude the target values, alter the values, reject the values, or pass them through.
- Lowpass
Removes high-frequency data from a signal, such as high-frequency noise and data peaks. This filter type has a smoothing effect on the data.
- Highpass
Removes low frequency data from a signal, such as a bias or offset. This filter type preserves sudden changes and peaks in a signal.
- Bandpass
Preserves the data from a signal with frequency characteristics at the intersection between the highpass and lowpass filters. This filter type is good at removing a bias and smoothing a signal.
Bandpass filters are created by combining a highpass and a lowpass filter. The highpass filter cutoff frequency represents the lower cutoff, and the lowpass filter frequency represents the higher cutoff.
- Bandstop
Removes data from a signal with frequency characteristics that are rejected by the low pass and the highpass filters. This filter type is good at preserving the signal bias and sudden changes.
Depending on the type of filter you chose, set one or more cutoff values.
Use the High cutoff and Low cutoff options to define an upper and/or a lower threshold for values. One or both of these options are required to specify which values are rejected or passed through.
A bandstop or bandpass filter requires that you set both high and low cutoff values.
Other filter types, such as the lowpass filter, require that you set only the low cutoff value.
Select the Scale option if scaling should be applied to coefficients; otherwise leave blank.
Connect the filter to Apply Filter, and connect a dataset.
Use the column selector to specify which columns the filter should be applied to. By default, the Apply Filter module will use the filter for all selected numeric columns.
Run the experiment.
Note that the FIR Filter module does not provide the option to create an indicator column. Column values are always transformed in place.
For examples of how filters are used in machine learning, see this experiment in the Model Gallery:
The Filters experiment demonstrates all filter types. The example uses an engineered waveform dataset to more easily illustrate the effects of the different filters.
FIR filters have these characteristics:
FIR filters do not have feedback; that is, they use the previous filter outputs.
FIR filters are more stable, because the impulse response will always return to 0.
FIR filters require a higher order to achieve the same selectivity as infinite impulse response (IIR) filters.
Like other filters, the FIR filter can be designed with a specific cutoff frequency that preserves or rejects frequencies that compose the signal.
The window type determines the trade-off between selectivity (width of the transition band in which frequencies are neither fully accepted nor rejected) and suppression (the total attenuation of frequencies to be rejected).
The windowing function is applied to the ideal filter response to force the frequency response to zero outside of the window. Coefficients are selected by sampling the frequency response within the window.
The number of coefficients returned by the FIR Filter module is equal to the filter order plus one. The coefficient values are determined by filter parameters and by the windowing method, and are symmetric to guarantee a linear phase response
See the Technical Notes section for an example of the trade-offs between these characteristics.
The FIR Filter module returns filter coefficients, or tap weights, for the created filter.
The coefficients are determined by the filter, based on the parameters you enter (such as the order). If you want to specify custom coefficients, use the User-Defined Filter module.
When Scale is set to True, filter coefficients will be normalized, such that the magnitude response of the filter at the center frequency of the passband is 0. The implementation of normalization in Azure Machine Learning Studio is the same as in the fir1 function in MATLAB.
Typically, in the window design method, you design an ideal infinite impulse response (IIR) filter. The window function is applied to the waveform in the time domain, and multiplies the infinite impulse response by the window function. This results in the frequency response of the IIR filter being convolved with the frequency response of the window function. However, in the case of FIR filters, the input and filter coefficients (or tap weights) are convolved as follows when applying the filter:
y=b*x
Let n be the length of the input signal and m be the number of taps.
Then y j =∑ m i=1 b i x j−(i−1) j=1..n where the values xj-(i-1)=0 if j-(i-1) < 1.
The following table compares selectivity with stop band attenuation for a FIR filter with length n by using different windowing methods:
Window Type | Transition Region | Minimum Stopband Attenuation |
|---|---|---|
Rectangular | 0.041n | 21 dB |
Triangle | 0.11n | 26 dB |
Hann | 0.12n | 44 dB |
Hamming | 0.23n | 53 dB |
Blackman | 0.2n | 75 dB |
Name | Range | Type | Default | Description |
|---|---|---|---|---|
Order | >=4 | Integer | 5 | Specify the filter order |
Window | Any | WindowType | Specify the type of window to apply | |
Filter type | Any | FilterType | LowPass | Select the type of filter to create |
Low cutoff | [double.Epsilon;.9999999] | Float | 0.3 | Set the low cutoff frequency |
High cutoff | [double.Epsilon;.9999999] | Float | 0.7 | Set the high cutoff frequency |
Scale | Any | Boolean | True | If true, filter coefficients will be normalized |
Name | Type | Description |
|---|---|---|
Filter | Filter implementation |
For a list of all exceptions, see Machine Learning REST API Error Codes.
Exception | Description |
|---|---|
NotInRangeValue | Exception occurs if parameter is not in range. |