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dsp.FrequencyDomainFIRFilter

Filter input signal in frequency domain

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Description

The dsp.FrequencyDomainFIRFilter System object™ implements frequency-domain, fast Fourier transform (FFT)-based filtering to filter a streaming input signal. In the time domain, the filtering operation involves a convolution between the input and the impulse response of the finite impulse response (FIR) filter. In the frequency domain, the filtering operation involves the multiplication of the Fourier transform of the input and the Fourier transform of the impulse response. The frequency-domain filtering is efficient when the impulse response is very long. You can specify the filter coefficients directly in the frequency domain by setting NumeratorDomain to "Frequency".

This object uses the overlap-save and overlap-add methods to perform the frequency-domain filtering. For an example showing these two methods, see Frequency Domain Filtering Using Overlap-Add and Overlap-Save. For filters with a long impulse response length, the latency inherent to these two methods can be significant. To mitigate this latency, the dsp.FrequencyDomainFIRFilter object partitions the impulse response into shorter blocks and implements the overlap-save and overlap-add methods on these shorter blocks. To partition the impulse response, set the PartitionForReducedLatency property to true. For an example, see Reduce Latency Through Partitioned Numerator. For more details on these two methods and on reducing latency through impulse response partitioning, see Algorithms.

The dsp.FrequencyDomainFIRFilter object can model single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) systems by supporting multiple filters in the time domain and the frequency domain. For examples, see Filter Input Signal Using 1-by-2 SIMO System and Filter Input Signal Using 3-by-2 MIMO System. You can also specify multiple paths between each input channel and output channel pair using the NumPaths property. For an example, see Filter Input Signal Through Multiple Propagation Paths. For more information on modeling MIMO systems, see Modeling MIMO System with Multiple Propagation Paths. (since R2023b)

To filter the input signal in the frequency domain:

  1. Create the dsp.FrequencyDomainFIRFilter object and set its properties.

  2. Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

Creation

Syntax

fdf = dsp.FrequencyDomainFIRFilter
fdf = dsp.FrequencyDomainFIRFilter(num)
fdf = dsp.FrequencyDomainFIRFilter(Name=Value)

Description

fdf = dsp.FrequencyDomainFIRFilter creates a frequency domain FIR filter System object that filters each channel of the input signal independently over time in the frequency domain using the overlap-save or overlap-add method.

fdf = dsp.FrequencyDomainFIRFilter(num) creates a frequency domain FIR filter object with the Numerator property set to num.

Example: dsp.FrequencyDomainFIRFilter(fir1(400,2*2000/8000))

example

fdf = dsp.FrequencyDomainFIRFilter(Name=Value) creates a frequency domain FIR filter System object with each specified property set to the specified value. You can use this syntax with any previous input argument combinations.

Example: dsp.FrequencyDomainFIRFilter(Method="Overlap-add")

example

Properties

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Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the release function unlocks them.

If a property is tunable, you can change its value at any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

Frequency-domain filter method, specified as either "Overlap-save" or "Overlap-add". For more details on these two methods, see Algorithms.

Domain of the filter coefficients, specified as one of the following:

  • "Time" –– Specify the time-domain filter numerator in the Numerator property.

  • "Frequency" –– Specify the filter's frequency response in the FrequencyResponse property.

FIR filter coefficients, specified as a row vector (single filter) or a matrix (multiple filters) (since R2023b) of size F-by-NumLen, where F is the number of filters and NumLen is the filter length. If F is greater than 1, then its value must be a multiple of the product of the number of input channels (columns) T and the number of paths P you specify through the NumPaths property. The multiplication factor determines the number of output channels R and equals F/(T × P).

The coefficient values can change during simulation but the size of the numerator must remain constant.

Tunable: Yes

Dependencies

To enable this property, set NumeratorDomain to "Time".

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64
Complex Number Support: Yes

Frequency response of the filter, specified as a row vector, matrix, or a 3-D array (since R2024a).

When you set PartitionForReducedLatency to true, FrequencyResponse must be a matrix of size 2PL-by-N to represent a single filter or a 3-D array of size 2PL-by-N-by-F to represent multiple filters (since R2024a), where PL is the partition size, N is the number of partitions, and F is the number of filters.

When you set PartitionForReducedLatency to false, FrequencyResponse can be a row vector or a matrix of size F-by-FFTLength. If F is greater than 1 (multiple filters), then its value must be a multiple of the product of the number of input channels (columns) T and the number of paths P you specify through the NumPaths property. The multiplication factor determines the number of output channels R and equals F/(T × P). (since R2023b)

Tunable: Yes

Dependencies

To enable this property, set NumeratorDomain to "Frequency".

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64
Complex Number Support: Yes

Time-domain numerator length, specified as a positive integer.

Dependencies

To enable this property, set NumeratorDomain to "Frequency" and PartitionForReducedLatency to false.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Flag to specify if the filter coefficients are all real, specified as true or false.

Dependencies

To enable this property, set NumeratorDomain to "Frequency".

Flag to partition numerator to reduce latency, specified as:

  • false –– The filter uses the traditional overlap-save or overlap-add method. The latency in this case is FFTLength – length(Numerator) + 1.

  • true –– In this mode, the object partitions the numerator into segments of length specified by the PartitionLength property. The filter performs overlap-save or overlap-add on each partition, and combines the partial results to form the overall output. The latency is now reduced to the partition length.

FFT length, specified as a positive integer. The default value of this property, [], indicates that the FFT length is equal to twice the numerator length. The FFT length must be greater than or equal to the numerator length.

Dependencies

To enable this property, set NumeratorDomain property to "Time" and PartitionForReducedLatency property to false.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Numerator partition length PL, specified as a positive integer less than or equal to the length of the numerator.

Dependencies

To enable this property, set the NumeratorDomain property to "Time" and PartitionForReducedLatency property to true.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

This property is read-only.

Filter latency in samples, returned as an integer greater than 0. When PartitionForReducedLatency is:

  • false –– The latency is equal to FFTLength – length(Numerator) + 1.

  • true –– The latency is equal to the partition length.

Data Types: uint32

Since R2023b

Number of propagation paths P between each input channel and output channel pair, specified as a positive integer. Each path is represented by a unique filter. When you specify the number of paths to be greater than 1, the filter models multipath propagation. The filtered output contributions from all paths between each input channel and output channel pair are summed up.

For an example, see Filter Input Signal Through Multiple Propagation Paths.

Dependencies

To enable this property, the number of filters F must be greater than 1.

Data Types: single | double

Since R2023b

Option to sum filtered output contributions from all input channels, specified as:

  • true –– The object adds the filtered output from each input channel to generate an L-by-R output matrix, where L is the input frame size (number of input rows) and R is the output channels. R equals F/(T x P), where F is the number of filters, T is the number of input channels, and P is the value that you specify in the NumPaths property.

  • false –– The object does not sum filtered output contributions from all input channels. The output is an L-by-R-by-T array.

For more information on how the object computes the output based on the value of the SumFilteredOutputs property, see Modeling MIMO System with Multiple Propagation Paths.

Dependencies

To enable this property, the number of filters F must be greater than 1.

Data Types: logical

Usage

Syntax

output = fdf(input)

Description

output = fdf(input) filters the input signal and outputs the filtered signal. The object filters each channel of the input signal independently over time in the frequency domain.

example

Input Arguments

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Data input, specified as a matrix of size L-by-T. This object supports variable-size input signals, that is, you can change the input frame size (number of rows) even after calling the algorithm. However, the number of channels (columns) must remain constant.

Data Types: single | double
Complex Number Support: Yes

Output Arguments

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Filtered output, returned as a vector, matrix, or a 3-D array.

When the number of filters F is greater than 1 and you set SumFilteredOutputs to:

  • true –– The object adds the filtered output from each input channel to generate an L-by-R output matrix, where L is the input frame size (number of input rows) and R is the number of output channels. R equals F/(T x P), where F is the number of filters, T is the number of input channels, and P is the value that you specify in the NumPaths property.

  • false –– The object does not sum filtered output contributions from all input channels. The output is an L-by-R-by-T array. output(:,j,k) refers to the output from the kth input channel for the jth output channel. For example, output(:,3,2) indicates output on the third output channel from the second input channel.

(since R2023b)

For more information on how the object computes the output, see Algorithms.

The output has the same data type and complexity as the input signal.

Data Types: single | double
Complex Number Support: Yes

Object Functions

To use an object function, specify the System object as the first input argument. For example, to release system resources of a System object named obj, use this syntax:

release(obj)

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visualizeVisualize frequency response of individual filter stages or sum of filter paths between input and output channels
stepRun System object algorithm
releaseRelease resources and allow changes to System object property values and input characteristics
resetReset internal states of System object

Examples

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Open Script

Filter input signal using overlap-add and overlap-save methods, and compare the outputs to the output of a time-domain FIR filter.

Initialization

Design the FIR lowpass filter coefficients using the designLowpassFIR function. The sampling frequency is 8 kHz. Specify a filter order of 400 and a cutoff frequency of 2 kHz. The impulse response has a length of 401.

order   = 400;
Fs      = 8000;
Fcutoff = 2000;
imp     = designLowpassFIR(FilterOrder=order,CutoffFrequency=2*Fcutoff/Fs);

Create two dsp.FrequencyDomainFIRFilter objects and a dsp.FIRFilter object. Set the numerator of all the three filters to imp. Delay the FIR output by the latency of the frequency-domain filter.

fdfOA = dsp.FrequencyDomainFIRFilter(imp,Method="Overlap-add");
fdfOS = dsp.FrequencyDomainFIRFilter(imp,Method="Overlap-save");
fir = dsp.FIRFilter(Numerator=imp);
dly = dsp.Delay(Length=fdfOA.Latency);

Create two dsp.SineWave objects. The sine waves have a sample rate of 8000 Hz, frame size of 256, and frequencies of 100 Hz and 3 kHz, respectively. Create a timescope object to view the filtered outputs.

frameLen  = 256;
sin_100Hz = dsp.SineWave(Frequency=100,SampleRate=Fs,...
    SamplesPerFrame=frameLen);
sin_3KHz  = dsp.SineWave(Frequency=3e3,SampleRate=Fs,...
    SamplesPerFrame=frameLen);
ts = timescope(TimeSpanSource="property",TimeSpan=5*frameLen/Fs,...
    YLimits=[-1.1 1.1],...
    ShowLegend=true,...
    SampleRate=Fs,...
    ChannelNames={"Overlap-add","Overlap-save","Direct-form FIR"});

Streaming

Stream 1e4 frames of noisy input data. Pass this data through the frequency domain filters and the time-domain FIR filter. View the filtered outputs in the time scope.

numFrames = 1e4;
for idx = 1:numFrames
    x = sin_100Hz() + sin_3KHz() + 0.01*randn(frameLen,1);
    yOA = fdfOA(x);
    yOS = fdfOS(x);
    yFIR = fir(dly(x));
    ts([yOA,yOS,yFIR]);
end

The outputs of all the three filters match exactly.

Open Script

Partition the impulse response length of a frequency domain FIR filter. Compare the outputs of the partitioned filter and the original filter.

Design the FIR lowpass filter coefficients using the designLowpassFIR function. The sampling frequency is 8 kHz. Specify a filter order of 4000 and a cutoff frequency of 2 kHz. The impulse response is of length 4001.

order   = 4000;
Fs      = 8000;
Fcutoff = 2000;
imp     = designLowpassFIR(FilterOrder=order,CutoffFrequency=2*Fcutoff/Fs);

Create a dsp.FrequencyDomainFIRFilter with coefficients set to the imp vector. The latency of this filter is given by $FFT Length - Length(Numerator) + 1$, which is equal to 4002. By default, FFT length is equal to twice the numerator length. This makes the latency proportional to the impulse response length.

fdfOS  = dsp.FrequencyDomainFIRFilter(imp,Method="Overlap-save");
fprintf('Frequency domain filter latency is %d samples\n',fdfOS.Latency);

Frequency domain filter latency is 4002 samples

Partition the impulse response to blocks of length 256. The latency after partitioning is proportional to the block length.

fdfRL  = dsp.FrequencyDomainFIRFilter(imp,Method="Overlap-save",...
    PartitionForReducedLatency=true,...
    PartitionLength=256);
fprintf('Frequency domain filter latency is %d samples\n',fdfRL.Latency);

Frequency domain filter latency is 256 samples

Compare the outputs of the two frequency domain filters. The latency of fdfOS is 4002, and the latency of fdfRL is 256. To compare the two outputs, delay the input to fdfRL by 4002 - 256 samples.

dly = dsp.Delay(Length=(fdfOS.Latency-fdfRL.Latency));

Create two dsp.SineWave objects. The sine waves have a sample rate of 8000 Hz, frame size of 256, and frequencies of 100 Hz and 3 kHz, respectively. Create a timescope object to view the filtered outputs.

frameLen  = 256;
sin_100Hz = dsp.SineWave(Frequency=100,SampleRate=Fs,...
    SamplesPerFrame=frameLen);
sin_3KHz  = dsp.SineWave(Frequency=3e3,SampleRate=Fs,...
    SamplesPerFrame=frameLen);
ts = timescope(TimeSpanSource="property",TimeSpan=5*frameLen/Fs,...
    YLimits=[-1.1 1.1],...
    ShowLegend=true,...
    SampleRate=Fs,...
    ChannelNames={'Overlap-save With Partition','Overlap-save Without Partition'});

Stream 1e4 frames of noisy input data. Pass this data through the two frequency domain filters. View the filtered outputs in the time scope. The outputs match exactly.

numFrames = 1e4;
for idx = 1:numFrames
    x = sin_100Hz() + sin_3KHz() + .1 * randn(frameLen,1);
    yRL = fdfRL(dly(x));
    yOS = fdfOS(x);
    ts([yRL,yOS]);
end

Open Script

Specify the numerator coefficients of the frequency-domain FIR filter in the frequency domain. Filter the input signal using the overlap-add method. Compare the frequency-domain FIR filter output to the corresponding time-domain FIR filter output.

Initialization

Design the FIR lowpass filter coefficients using the designLowpassFIR function. The sampling frequency is 8 kHz, and the cutoff frequency of the filter is 2 kHz. The time-domain impulse response has a length of 401. Compute the FFT of this impulse response and specify this response as the frequency response of the frequency-domain FIR filter. Set the time-domain numerator length, specified by the NumeratorLength property, to the number of elements in the time-domain impulse response.

order   = 400;
Fs      = 8000;
Fcutoff = 2000;
imp     = designLowpassFIR(FilterOrder=order,CutoffFrequency=2*Fcutoff/Fs);
H   = fft(imp,2*numel(imp));
oa  = dsp.FrequencyDomainFIRFilter(NumeratorDomain="Frequency",...
    FrequencyResponse=H,...
    NumeratorLength=numel(imp),...
    Method='Overlap-add');
fprintf('Frequency domain filter latency is %d samples\n',oa.Latency);

Frequency domain filter latency is 402 samples

Create a dsp.FIRFilter System object and specify the numerator as the time-domain coefficients computed using the designLowpassFIR function, imp. Delay the FIR output by the latency of the frequency-domain FIR filter.

fir = dsp.FIRFilter(Numerator=imp);
dly = dsp.Delay(Length=oa.Latency);

Create two dsp.SineWave objects. The sine waves generated have a sample rate of 8000 Hz, frame size of 256, and frequencies of 100 Hz and 3 kHz, respectively. Create a timescope object to view the filtered outputs.

frameLen  = 256;

sin_100Hz = dsp.SineWave(Frequency=100,SampleRate=Fs,...
    SamplesPerFrame=frameLen);
sin_3KHz  = dsp.SineWave(Frequency=3e3,SampleRate=Fs,...
    SamplesPerFrame=frameLen);

ts = timescope(YLimits=[-1.1 1.1],...
    TimeSpanSource="property",TimeSpan=5*frameLen/Fs,...
    ShowLegend=true,...
    SampleRate=Fs,...
    ChannelNames={'Overlap-add','Direct-form FIR'});

Streaming

Stream 1e4 frames of noisy input data. Pass this data through the frequency-domain FIR filter and the time-domain FIR filter. View the filtered outputs in the time scope. The outputs of both the filters match exactly.

numFrames = 1e4;
for idx = 1:numFrames
    x = sin_100Hz() + sin_3KHz() + 0.01 * randn(frameLen,1);
    y1 = oa(x);
    y2 = fir(dly(x));
    ts([y1,y2]);
end

Since R2024a

Open Live Script

Filter an input signal using a 1-by-2 SIMO system with two distinct paths between the input and each output. Partition the filters to reduce latency.

Design four lowpass FIR filters, each with a different cutoff frequency, specified in normalized frequency units. The filter order for each filter is 4000 and the sampling rate is 8000 Hz. The SIMO system models one input channel, two output channels, and two paths between each input channel-output channel pair.

order = 4000;
Fs   = 8000;
num  = [ % Path 1, Output Channel 1
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.2);...
    % Path 2, Output Channel 1
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.3);...
    % Path 1, Output Channel 2
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.4);...
    % Path 2, Output Channel 2
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.5)];

Initialize the dsp.FrequencyDomainFIRFilter object with the array of filters. Set the partition length to 256 and the number of paths to 2. The object uses the Overlap-save method by default.

obj = dsp.FrequencyDomainFIRFilter(Numerator=num,...
    PartitionForReducedLatency=true, PartitionLength=256, NumPaths=2)
obj = 
  dsp.FrequencyDomainFIRFilter with properties:

                        Method: 'Overlap-save'
               NumeratorDomain: 'Time'
                     Numerator: [4x4001 double]
                      NumPaths: 2
            SumFilteredOutputs: true
    PartitionForReducedLatency: true
               PartitionLength: 256
                       Latency: 256

Visualize the frequency response of the individual filters.

visualize(obj)

The input contains two sinusoidal signals, each with a frame length of 256. The first sinusoidal signal has a frequency of 500 Hz (or 0.125 in normalized frequency units) and the second sinusoidal signal has a frequency of 1000 Hz (or 0.25 in normalized frequency units).

frameLen  = 256;
sin_500Hz = dsp.SineWave(Frequency=500,SampleRate=Fs,...
    SamplesPerFrame=frameLen);
sin_1KHz  = dsp.SineWave(Frequency=1e3,SampleRate=Fs,...
    SamplesPerFrame=frameLen);

Initialize a spectrumAnalyzer object to view the spectrum of the input and the filtered output.

specScope = spectrumAnalyzer(SampleRate=Fs,PlotAsTwoSidedSpectrum=false,...
    ChannelNames={'Input','Output Channel 1',...
    'Output Channel 2'},...
    ShowLegend=true);

Stream in 1e4 frames of the noisy input sinusoidal signal. The input noise is white Gaussian with a mean of 0 and a variance of 0.01. Pass the signal through the designed filter. Visualize the spectrum of the input and output signals in the spectrum analyzer.

for idx = 1:1e4
    x = sin_500Hz() + sin_1KHz() + 0.01 * randn(frameLen, 1);
    y = obj(x);
    specScope([x, y]);
end

Visualize the frequency response of the sum of all filter paths between each input channel and output channel by setting SumFilterPaths to true.

visualize(obj,SumFilterPaths=true)

Since R2023b

Open Live Script

Design six lowpass FIR filters with varying cutoff frequencies. The filter order for each filter is 400 and the sampling rate is 8000 Hz. The system models three input channels, two output channels, and one path between each input channel-output channel pair.

order = 400;
Fs = 8000;
num = [% Input Channel 1, Output Channel 1
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.1);...
    % Input Channel 1, Output Channel 2
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.15);...
    % Input Channel 2, Output Channel 1
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.2);...
    % Input Channel 2, Output Channel 2
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.25);...
    % Input Channel 3, Output Channel 1
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.3);...
    % Input Channel 3, Output Channel 2
    designLowpassFIR(FilterOrder=order,CutoffFrequency=0.35)];

Initialize the dsp.FrequencyDomainFIRFilter object with the array of filters. Specify the filtering method as "Overlap-save". The SumFilteredOutputs property is true by default.

 filt = dsp.FrequencyDomainFIRFilter(Numerator=num,...
    Method='Overlap-save')
filt = 
  dsp.FrequencyDomainFIRFilter with properties:

                        Method: 'Overlap-save'
               NumeratorDomain: 'Time'
                     Numerator: [6x401 double]
                      NumPaths: 1
            SumFilteredOutputs: true
    PartitionForReducedLatency: false
                     FFTLength: []
                       Latency: 402

The input contains two sinusoidal signals each with a frame length of 256. The first sinusoidal signal contains tones at 100 Hz, 200 Hz, and at 300 Hz. The second sinusoidal signal contains tones at 2 kHz, 2.5 kHz, and at 3 kHz.

 frameLen = 256;
 sin_Hz = dsp.SineWave(Frequency=[100 200 300],SampleRate=Fs,...
     SamplesPerFrame=frameLen);
 sin_KHz = dsp.SineWave(Frequency=[2e3 2.5e3 3e3],SampleRate=Fs,...
     SamplesPerFrame=frameLen);

Initialize a spectrumAnalyzer object to view the spectrum of the input and the filtered output.

 specScope = spectrumAnalyzer(SampleRate=Fs,PlotAsTwoSidedSpectrum=false, ...
     ChannelNames={'Input Channel 1','Input Channel 2','Input Channel 3', ...
     'Output Channel 1','Output Channel 2'},ShowLegend=true);

Stream in 1e4 frames of the noisy input sinusoidal signal. The input noise is white Gaussian with a mean of 0 and a variance of 0.01. Pass the signal through the designed filter. Visualize the spectrum of the input and output signals in spectrum analyzer.

  for idx = 1:1e4
     x = sin_Hz()+sin_KHz()+0.01*randn(frameLen,3);
     y = filt(x);
     specScope([x,y]);
 end

Since R2023b

Open Live Script

Filter an input signal through three distinct paths between the input and the output by specifying three filters to model the frequency responses across each path.

Design multiple lowpass FIR filters with varying fractional delays. The filter order for each filter is 400 and the sampling rate is 8000 Hz.

order = 400;
Fs = 8000;
num = [designFracDelayFIR(0.1,order); % Path 1
    designFracDelayFIR(0.2,order); % Path 2
    designFracDelayFIR(0.3,order); % Path 3
    ];

Initialize the dsp.FrequencyDomainFIRFilter object with the array of filters. Set the filtering method to "Overlap-add" and the number of propagation paths to 3. The object models a single-input single-output (SISO) system. The SumFilteredOutputs property is true by default.

filt = dsp.FrequencyDomainFIRFilter(Numerator=num, ...
    Method='overlap-add',NumPaths=3)
filt = 
  dsp.FrequencyDomainFIRFilter with properties:

                        Method: 'Overlap-add'
               NumeratorDomain: 'Time'
                     Numerator: [3x400 double]
                      NumPaths: 3
            SumFilteredOutputs: true
    PartitionForReducedLatency: false
                     FFTLength: []
                       Latency: 401

The input contains two sinusoidal signals each with a frame length of 1024. The first sinusoidal signal contains a tone at 200 Hz. The second sinusoidal signal contains a tone at 4 kHz.

frameLen = 1024;
sin_200Hz = dsp.SineWave(Frequency=200,SampleRate=Fs, ...
    SamplesPerFrame=frameLen);
sin_4KHz = dsp.SineWave(Frequency=4e3,SampleRate=Fs, ...
    SamplesPerFrame=frameLen);

Initialize a spectrumAnalyzer object to view the spectrum of the input and the filtered output.

specScope = spectrumAnalyzer(SampleRate=Fs,PlotAsTwoSidedSpectrum=false, ...
    ChannelNames={'Input Channel','Output Channel'},ShowLegend=true);

Stream in 1e3 frames of the noisy input sinusoidal signal. The input noise is white Gaussian with a mean of 0 and a variance of 0.01. Pass the signal through the designed filter. Visualize the spectrum of the input and output signals in spectrum analyzer.

for idx = 1:1e3
    x = sin_200Hz()+sin_4KHz()+0.01*randn(frameLen,1);
    y = filt(x);
    specScope([x,y])
end

Algorithms

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Overlap-save and overlap-add are the two frequency-domain FFT-based filtering methods this algorithm uses.

References

[1] Stockham, T. G., Jr. "High Speed Convolution and Correlation." Proceedings of the 1966 Spring Joint Computer Conference, AFIPS, Vol 28, 1966, pp. 229–233.

Extended Capabilities

Version History

Introduced in R2017b

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See Also

Functions

Objects

Blocks