# freqzmr

Compute DTFT approximation of the impulse response of a multirate or a single-rate filter

Since R2024a

## Description

example

[Y,Fout] = freqzmr(filtObj) outputs the Discrete-Time Fourier Transform (DTFT) of the impulse response of the filter object and the corresponding frequencies.

If the input filter is a single-rate FIR or IIR filter, the behavior of freqzmr and freqz functions is equivalent. If the filter is an irreducible multirate filter, use the freqzmr function to analyze the frequency response of the filter. For more details on the differences between these two functions, see Comparison of freqzmr and freqz functions. For more information on reducible and irreducible multirate filters, see Overview of Multirate Filters.

example

freqzmr(filtObj) plots the magnitude and phase of the impulse response DTFT. The function also color-codes the distortion that occurs due to the rate changes. For more information on distortion, see Output Distortion.

## Examples

collapse all

Compute and plot the impulse response DTFT of the FIR rate converter using the freqzmr function.

firRC = dsp.FIRRateConverter(5,7);
freqzmr(firRC)

Compute the impulse response DTFT of the sample rate converter using the freqzmr function.

SRC = dsp.SampleRateConverter(InputSampleRate=77,...
OutputSampleRate=32*24,...
Bandwidth=23,...
StopbandAttenuation=120);

[Y,F] = freqzmr(SRC,MinimumFFTLength=1024);

When you do not specify any output arguments, the function plots the output DTFT on a convenience plot.

Plot the impulse response DTFT with maximum distortion sensitivity. Note that in the red regions, one can expect some distortion due to the rate conversion aliasing.

freqzmr(SRC,MinimumFFTLength=1024,DistortionThreshold=-inf);

To hide distortion coloring, set DistortionThreshold to 0.

freqzmr(SRC,MinimumFFTLength=1024,DistortionThreshold=0);

Specify the frequency range to be "centered".

freqzmr(SRC,MinimumFFTLength=1024,DistortionThreshold=-inf,FrequencyRange="centered");

To compare, pass a random white noise signal to the sample rate converter. View the spectrum of the corresponding output in the spectrum analyzer.

You can see that the impulse response DTFT that the freqzmr function computes is exactly the same as the spectrum of the output signal when you input a random white noise signal.

sa = spectrumAnalyzer(SampleRate=SRC.OutputSampleRate,PlotAsTwoSidedSpectrum=true,YLimits=[-200 20]);

for k=1:10
u = randn(4096,1);
y = SRC(u);
sa(y);
end

Design a composite filter which is a cascade of these filters:

• A single-rate filter.

• A parallel structure of multirate filters.

Design the single-rate filter using the dsp.LowpassFilter object in its default configuration.

singleRateFilter = dsp.LowpassFilter
singleRateFilter =
dsp.LowpassFilter with properties:

FilterType: 'FIR'
DesignForMinimumOrder: true
PassbandFrequency: 8000
StopbandFrequency: 12000
PassbandRipple: 0.1000
StopbandAttenuation: 80
NormalizedFrequency: false
SampleRate: 44100

Show all properties

The parallel structure contains a cascade of dsp.FIRInterpolator and dsp.FIRDecimator objects, and a dsp.FIRRateConverter object.

Design the dsp.FIRInterpolator and the dsp.FIRRateConverter objects using the designBandpassFIR function.

B1 = 2.5*designBandpassFIR(FilterOrder=256,...
CenterFrequency=0.3,Bandwidth=1/5);
B2 = 0.25*designBandpassFIR(FilterOrder=256,...
CenterFrequency=0.7,Bandwidth=1/20);

Create the parallel structure of multirate filters using the parallel and cascade functions.

dsp.FIRDecimator(2)),dsp.FIRRateConverter(10,4,B2))
multirateFilter =
dsp.ParallelFilter with properties:

Branch2: [1×1 dsp.FIRRateConverter]
CloneBranches: true

Create a composite filter which is a cascade of the single-rate filter and the parallel filter.

compositeFilter =

Stage1: [1×1 dsp.LowpassFilter]
Stage2: [1×1 dsp.ParallelFilter]
CloneStages: true

Compute and plot the impulse response DTFT of this filter using the freqzmr function. Set MinimumFFTLength to 512. Set the frequency range to be "centered".

freqzmr(compositeFilter,MinimumFFTLength=512,FrequencyRange='centered')

## Input Arguments

collapse all

### Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: src = dsp.SampleRateConverter(InputSampleRate=77,OutputSampleRate=32*24,Bandwidth=23,StopbandAttenuation=120); [Y,Fout] = freqzmr(src,MinimumFFTLength=1024,DistortionThreshold=-inf);

Shortest discrete spectrum length, specified as a positive integer. When the function resamples the spectrum internally (for example during downsampling), the size of the resampled spectrum is no shorter than MinimumFFTLength. For more information, see DTFT of Downsampled Signal.

Data Types: double

Sample rate of input signal FsIn in Hz, specified as a positive integer.

If you do not specify the input sample rate, the function inherits this value from the filter object. If the input filter object contains an explicit property to specify the input sample rate, the function uses that property value. If the filter object does not contain any such property, the function uses a value of 1.

Data Types: double

Frequency range of the DTFT, specified as one of these:

• "auto" –– The function automatically determine the appropriate choice ("onesided" or "twosided") based on the system symmetry. The function chooses "onesided" when the system spectrum is Hermitian symmetric, and chooses "twosided" otherwise.

• "onesided" –– Impulse response DTFT contains only the positive frequencies. Frequency range is [0 Fsout/2].

• "twosided" –– Frequency range is [0 Fsout].

• "centered" –– Frequency range is [−Fsout/2 Fsout/2].

Fsout is the output sample rate and is given by the equation, Fsout = FsIn×(L/M), where L/M is the rate conversion ratio of the input filter object.

Output ambiguity distortion threshold in dB of the convenience magnitude plot, specified as a scalar in the range (−∞ 0]. The DistortionThreshold argument is the threshold over which the signal is considered ambiguous.

Decrease the threshold value for a more sensitive detection of distortion. The function colors the convenience magnitude plot according to the distortion ratio. Red indicates maximum distortion (two or more branches have the same power), while blue indicates no distortion. If the distortion threshold is 0 dB, the function hides all distortion coloring.

The function ignores any distortion value below the value of DistortionThreshold and treats the output as non-ambiguous.

Here is a plot of the impulse response DTFT of an FIR rate converter with DistortionThreshold set to −∞. This plot shows the most sensitive detection of distortion.

Increase the threshold value to −40 dB. The function ignores all distortions below −40 dB.

Increasing the threshold value to 0 dB shows no distortion on the plot since the function ignores all the distortion values.

Data Types: double

## Output Arguments

collapse all

DTFT approximation of impulse response, returned as a column vector. The length of the impulse response DTFT depends on the MinimumFFTLength value and the rate conversion factors of the multirate filter stages.

Data Types: double
Complex Number Support: Yes

Frequencies in Hz corresponding to the impulse response DTFT approximation, returned as a column vector. The length of Fout equals the length of Y. The range of Fout depends on the FrequencyRange you choose.

Data Types: double

collapse all

### Discrete-Time Fourier Transform (DTFT) and its relation to DFT and FFT

The discrete-time Fourier transform (DTFT) of a sequence x[m] sampled at rate T is a periodic complex-valued function and is given by:

${X}_{1/T}\left(f\right)={\sum _{m}x\left[m\right]e}^{-2\pi jf\cdot T\cdot m}$

where, T is the sample rate of the signal. The DTFT plays a central role in the analysis of discrete signals and systems.

For computational purposes, DTFT is sampled on a finite set of frequencies and can be expressed in terms of DFT and FFT.

### Comparison of freqzmr and freqz functions

The freqz function plots the frequency response of a single-rate FIR or IIR filter while the freqzmr function plots the DTFT approximation of the filter impulse response. This distinction stems from the fact that multirate filters do not have a frequency response in general because a single input tone can generate multiple output tones. The freqzmr function accounts for multirate operations in its DTFT approximation, while freqz assumes that the filter is a single-rate FIR or IIR filter.

Here are a few examples which illustrate the behavior of freqz and freqzmr for various filters.

Single-Rate Filters

For single-rate FIR or IIR filters, the frequency response is equal to the DTFT of their impulse response, and the behavior of freqzmr and freqz functions is equivalent.

Consider the dsp.LowpassFilter object which is a single-rate filter.

lpFilter = dsp.LowpassFilter

Plot the frequency response of this filter using freqz, and the DTFT of the impulse response using freqzmr. Overlay the two plots.

The two plots are exactly the same.

Multirate Filters

Multirate filters cannot be characterized by a single transfer function, and do not have a frequency response in the usual sense. However, multirate filters have a well defined impulse response, whose DTFT gives valuable information about the spectral content of their outputs.

The freqzmr function computes the DTFT approximation of the multirate filter impulse response, and accounts for multirate operations while doing so. This behavior allows the freqzmr function to capture multirate effects such as imaging and aliasing, while the freqz function does not.

Here is an example. This filter is an antialiasing FIR filter with a downsample factor of 4.

firDec = dsp.FIRDecimator(4)

The freqz function inspects this filter as a single-rate filter. The freqzmr function computes the DTFT approximation of the filter impulse response. The DTFT plot accounts for aliasing and looks different compared to the frequency response plot. The aliased components compensate to a flat response. The plot shows these components by coloring them in red. For more information, see Output Distortion.

Multirate filters can be reducible or irreducible. A multirate filter is said to be reducible if it can be modified through noble identities to produce a single-rate FIR or IIR filter, as firDec in the previous example. For more information, see Overview of Multirate Filters. If the input filter is a reducible multirate filter, you can use the freqz function to analyze the filter. The function analyzes the equivalent FIR or IIR stage, and treats the filter as a single-rate FIR or IIR filter.

Multirate Filter Reducing to a Single-Stage

Consider this FIR rate converter which is trivially reducible for it has a single FIR stage.

firrc = dsp.FIRRateConverter(3,2,triang(9))
firrc =
dsp.FIRRateConverter with properties:

Main
InterpolationFactor: 3
DecimationFactor: 2
NumeratorSource: 'Property'
Numerator: [9×1 double]

With an input sample rate of 1 kHz, the output sample rate is 1.5 kHz. Plot the frequency response of the filter using freqz, and the DTFT of the filter impulse response using freqzmr. You can see that there is distortion in the frequency range [0.4 0.75 kHz]. The DTFT plot shows this distortion.

Consider this filter which is a cascade of a lowpass filter and an FIR rate converter. This filter can be reduced to a single stage.

lpfilt = dsp.LowpassFilter(SampleRate=1e3,...
PassbandFrequency=80,StopbandFrequency=100,FilterType='IIR');
firrc = dsp.FIRRateConverter(3,4);
cascFilt =

Stage1: [1×1 dsp.LowpassFilter]
Stage2: [1×1 dsp.FIRRateConverter]
CloneStages: true

Plot the frequency response of this filter using freqz and the DTFT of the filter impulse response using freqzmr. Overlay and compare the two plots.

The two functions generate plots which look similar but are not the same. Also, the freqzmr plot shows distortion regions in red, where there is rate conversion aliasing.

Irreducible Multirate Filter

Lastly, if the input filter is an irreducible multirate filter, use the freqzmr function to analyze the filter in the frequency domain. For more information on irreducible filters, see Overview of Multirate Filters.

Consider this filter which is a cascade of two FIR rate converters. This filter is irreducible.

firrc1 = dsp.FIRRateConverter;
firrc2 = dsp.FIRRateConverter(3,4);
cascFilt =

Stage1: [1×1 dsp.FIRRateConverter]
Stage2: [1×1 dsp.FIRRateConverter]
CloneStages: true

When you try to analyze this filter with freqz, the function errors out.

To analyze this filter, use the freqzmr function, which in its default configuration shows the DTFT of the filter impulse response.

freqzmr(src)

To summarize, for irreducible multirate filters, use the freqzmr function. For single-rate FIR or IIR filters, the behavior of freqz and freqzmr is equivalent.

Here is a table that compares the two functions and lists the key differences.

freqzmrfreqz
Computation

DTFT approximation of filter impulse response h

DTFT(h)

Frequency response of the filter, which is the transfer function evaluated on the unit circle

Single-rate convolution filters
Reducible multirate filters

Performs analysis on the single-rate FIR or IIR equivalent. The plot looks different compared to the freqzmr plot.

Irreducible multirate filters
Captures multirate effects such as imaging an aliasing
Support for normalized frequencies

## Algorithms

collapse all

The freqzmr algorithm treats the input filter object as a combination of FIR or IIR (single-rate) filters and multirate operations (upsample and downsample). The function processes the DTFT through each stage consecutively. For more information on DTFT, see Discrete-Time Fourier Transform (DTFT) and its relation to DFT and FFT.

### Single-Rate FIR or IIR Filters

Here is the block diagram of a single-rate filter with an input sequence x[m] and an output sequence y[m].

where, h is the impulse response of the single-rate filter.

The freqzmr function computes the impulse response DTFT, that is, DTFT(h) .

### DTFT of Downsampled Signal

For a given input signal x[m], the output of the downsampling operation is given by y[n] = x[Mm].

The DTFT of the downsampled signal is periodic and its period 1/(MT) is 1/M of the period of X1/T.

${Y}_{1/\left(MT\right)}\left(f\right)=\frac{1}{M}\sum _{m=0}^{M-1}{X}_{1/T}\left(\frac{f-m/T}{M}\right)$

For multirate filters, the components ${X}_{1/T}\left(\frac{f-m/T}{M}\right)$ can overlap and interfere with each other resulting in aliasing. To resolve aliasing, it is important that one of the components dominates the rest in its value. This component contributes to the sum while the rest are 0 resolving the issue of aliasing. In most cases, this component corresponds to m=0 (baseband component), that is, Y(1/MT)(f)≅X1/T(f/M).

Multirate distortion is the level of ambiguity between these components. You can specify the threshold in dB over which the function considers the signal ambiguous using the DistortionThreshold property. For more information, see Output Distortion.

Computing the DTFT of Downsampled Signal

DTFT downsampling is done through reshaping of the DTFT samples vector and taking a sum across columns. To downsample a signal with DTFT samples vector X by a decimation factor M, the downsampled DTFT is:

NX = size(X,1);
Y = (1/M)*sum(reshape(X,NX/M,M),2);

Note that the DTFT array X is usually resampled at a higher frequency resolution (increase by NX) prior to reshaping. This is done in order to make NX length divisible by M, and to ensure that the resolution of the downsampled DTFT Y is no less than MinimumFFTLength.

### DTFT of Upsampled Signal

For a given input signal x[m], the output of the upsampling operation is given by:

$y\left[n\right]=\left\{\begin{array}{cc}x\left[k\right]& m=kL\\ 0& else\end{array}$

y[n] operates at a rate L/T. The DTFT of the upsampled signal y is identical to the DTFT of x, that is, YL/T(f) = X1/T(f). The only difference is in the period, which is L/T, that is, L times larger on the frequency domain.

The freqzmr function determines the DTFT of the upsampled signal by replicating the input DTFT samples vector L times.

function Y = upsampleDTFT(X,L)
Y = repmat(X,L,1);
end

### DTFT of Composite Filters (Cascade and Parallel)

For a cascade of filters, the freqzmr function computes the output DTFT by passing the output DTFT of each stage as the input DTFT of the following stage. The first stage input is an impulse.

InputSpectrum=freqzmr(H1))

If you have multiple stages in the cascade, the freqzmr function computes the impulse response DTFT using:

Y = X;
for k = 1:NumStages
Y = outputDTFT(Stage(k),Y,MinimumFFTLength);
end
end

Parallel Filters

For parallel filters, the DTFT output is a sum of the DTFTs of the filter outputs on the individual branches.

### Output Distortion

In multirate systems, an input frequency generates several output frequencies due to aliasing and imaging. As a result, an output frequency component is often a combination of several different frequency components.

The output DTFT of a multirate filter can be written as a sum of the components Q0, Q1, …., QM-1.

$Y\left(f\right)=\sum _{m=0}^{M-1}{Q}_{m}\left(f\right)$

where:

• M is the total decimation factor

• Q0, Q1, …., QM-1 are DTFT components that the function computes through rate change operations.

Ideally, only one branch contributes to the sum while the rest are 0 (no-aliasing), that is, Qm = 0 for all m except for one. As soon as more than one of the branches has a nonzero value, Y(f) has aliasing.

The distortion is the level of ambiguity of the frequency branches Qm(f) in the sum above. It is defined as the inverse power ratio between the dominant component and the average power of the remaining components.

$D\left(f\right)=\sqrt{\frac{{Q}_{m1}}{\left(M-1\right)\sum _{k=2}^{M}{|{Q}_{mk}\left(f\right)|}^{2}}}$

The distortion ratio ranges between 0 and 1 (or −∞ to 0 in dB), where higher values indicate more ambiguity. A 1:1 ratio (0 dB) indicates that there is no dominant component, and the signal is totally ambiguous. In contrast, a 0-ratio (−∞ dB) indicates that the output frequency is non-ambiguous.

The freqzmr function sorts m1, …., mM at every frequency according to the corresponding Qm magnitude values. Note that the distortion ratio is invariant under permutations, that is, changing the order of the branches does not alter the distortion. The distortion ratio is a real number between 0 and 1. These values indicate where aliasing is present in a multirate system.

The function colors the impulse response DTFT depending on the value of the distortion ratio. Red indicates maximum distortion (two or more branches have the same power), while blue indicates no distortion. The function ignores any distortion value below the value of DistortionThreshold.

The following figure shows the distortion ratio in two frequencies. In the lower frequency (f = 4.375 Hz), there is a fairly low distortion since the dominant lobe is about 80 dB above the rest of the lobes. In the higher frequency (f = 12.125 Hz), there is far more significant interference as alias lobe 4 is much closer to the dominant lobe 1. This is indicated by the red color on the freqzmr plot.

## Version History

Introduced in R2024a