signalTimeFrequencyFeatureExtractor

Streamline signal time-frequency feature extraction

Since R2024a

Description

Use a signalTimeFrequencyFeatureExtractor object to extract time-frequency-domain features from a signal. You can use the extracted features to train a machine learning model or a deep learning network.

Creation

Syntax

sFE = signalTimeFrequencyFeatureExtractor

sFE = signalTimeFrequencyFeatureExtractor(Name=Value)

Description

sFE = signalTimeFrequencyFeatureExtractor creates a signalTimeFrequencyFeatureExtractor object with default property values.

sFE = signalTimeFrequencyFeatureExtractor(Name=Value) sets property values to the signalTimeFrequencyFeatureExtractor object. For example, signalTimeFrequencyFeatureExtractor(FrameSize=30,FrameOverlapLength=6) creates a signalTimeFrequencyFeatureExtractor object that partitions a signal into overlapping 30-sample frames to extract time-frequency features from each frame.

example

Properties

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Main Properties

`FrameSize` — Number of samples per frame
positive integer

Number of samples per frame, specified as a positive integer. The object divides the signal into frames of the specified length and extracts features for each frame. If you do not specify FrameSize, or if you specify FrameSize as empty, the object extracts features for the whole signal.

Data Types: single | double

`FrameRate` — Number of samples between start of frames
positive integer

Number of samples between the start of frames, specified as a positive integer. The frame rate determines the distance in samples between the starting points of frames. If you specify FrameRate, then you must also specify FrameSize. If you do not specify FrameRate or FrameOverlapLength, then the object assumes FrameRate to be equal to FrameSize. You cannot specify FrameRate and FrameOverlapLength simultaneously.

Data Types: single | double

`FrameOverlapLength` — Number of overlapping samples between consecutive frames
positive integer

Number of overlapping samples between consecutive frames, specified as a positive integer. FrameOverlapLength must be less than or equal to the frame size. If you specify FrameOverlapLength, then you must also specify FrameSize. You cannot specify FrameOverlapLength and FrameRate simultaneously.

Data Types: single | double

`SampleRate` — Sample rate
`[]` (default) | positive scalar

Input sample rate, specified as a positive scalar in hertz.

If you do not specify SampleRate, the extract function of the object assumes the signal sampling rate as 2π Hz.

Data Types: single | double

`FeatureFormat` — Format of generated signal features
`"matrix"` (default) | `"table"`

Format of the signal features generated by the extract function, specified as one of these:

"matrix" — Columns correspond to feature values.
"table" — Each table variable corresponds to a feature value.

Note

You can generate features for multiple signals at once by specifying a datastore object input in the extract function. In this case, extract returns a cell array where each member corresponds to a feature matrix or table from a signal member of the datastore. The format of the generated features in each member follows the format specified in FeatureFormat.

Data Types: char | string

`IncompleteFrameRule` — Rule to handle incomplete frames
`"drop"` (default) | `"zeropad"`

Rule to handle incomplete frames, specified as one of these:

"drop" — Drop the incomplete frame and do not use it to compute features.
"zeropad" — Zero-pad the incomplete frame and use it to compute features.

This rule applies when the current frame size is less than the specified FrameSize property.

Data Types: char | string

`Transform` — Time-frequency analysis method
`"spectrogram"` (default) | `"synchrosqueezedspectrogram"` | `"emd"` | `"vmd"` | `"scalogram"` | `"synchrosqueezedscalogram"` | `"wavelet"` | `"waveletpacket"`

Time-frequency analysis method used to extract signal features from, specified as:

"spectrogram" — Short-time Fourier transform (stft)
"synchrosqueezedspectrogram" — Fourier synchrosqueezed transform (fsst)
"emd" — Empirical mode decomposition (emd)
"vmd" — Variational mode decomposition (vmd)
"scalogram" — Continuous wavelet transform (cwt (Wavelet Toolbox)) magnitude
"synchrosqueezedscalogram" — Wavelet synchrosqueezed transform (wsst (Wavelet Toolbox)) magnitude
"wavelet" — Maximal overlap discrete wavelet transform (modwt (Wavelet Toolbox))
"waveletpacket" — Maximal overlap discrete wavelet packet transform (modwpt (Wavelet Toolbox))

See Time-Frequency Gallery for more information on time-frequency analysis methods.

Depending on the value you set in Transform, you can extract the following time-frequency features.

Features to Extract	Supported Values for `Transform`
Features to Extract	`"spectrogram"`, `"synchrosqueezedspectrogram"`, `"synchrosqueezedscalogram"`	`"emd"`	`"vmd"`	`"scalogram"`	`"wavelet"`, `"waveletpacket"`
`SpectralKurtosis`	✓
`SpectralSkewness`	✓
`SpectralCrest`	✓
`SpectralFlatness`	✓
`SpectralEntropy`	✓
`TFRidges`	✓
`InstantaneousBandwidth`	✓
`InstantaneousFrequency`	✓	✓	✓		✓
`InstantaneousEnergy`		✓	✓		✓
`MeanEnvelopeEnergy`		✓
`WaveletEntropy`					✓
`TimeSpectrum`				✓
`ScaleSpectrum`				✓

Note

You must have a Wavelet Toolbox™ license to specify Transform as "scalogram", "synchrosqueezedscalogram", "wavelet", or "waveletpacket".
You cannot specify FrameSize, FrameRate, or FrameOverlapLength when Transform is "emd" or "vmd".

Data Types: char | string

`ScalarizationMethod` — Methods to convert feature vectors to scalar values
`timeFrequencyScalarFeatureOptions` object

Methods to convert feature vectors to scalar values, specified as a timeFrequencyScalarFeatureOptions object.

You can specify methods to extract scalar values from the Features to Extract. Specify scalarization methods for the feature extractor object by using the ScalarizationMethod name-value argument or the setScalarizationMethods function.

If you specify ScalarizationMethod, the signalTimeFrequencyFeatureExtractorobject returns the corresponding scalar values for each feature vector using the scalarization method.
To convert a feature vector to scalar feature values:
- You must enable the feature for extraction by setting the feature name in the signalTimeFrequencyFeatureExtractor object to true.
- You must specify the desired scalarization methods for each feature name using a cell array of character vectors or a string array and store the information in a timeFrequencyScalarFeatureOptions object.
After that, the extract function:
- Extracts the vectors corresponding to each enabled feature.
- Takes the list of scalarization methods compiled by the object and for each method computes the corresponding scalar value.
- Concatenates the vector features and the scalar features.
If you do not specify ScalarizationMethod, the signalTimeFrequencyFeatureExtractor object does not perform any scalarization.

For more information about scalarization methods, see Scalarization Methods for Domain-Specific Signal Features.

Features to Extract

You can enable signal features using name-value arguments from the time-frequency signal representation that you specify in Transform.

Example: signalTimeFrequencyFeatureExtractor(Transform="emd",InstantaneousEnergy=true,MeanEnvelopeEnergy=true) specifies a time-frequency feature extractor object to extract the instantaneous energy and mean envelope energy from the empirical mode decomposition (EMD) transform of a signal.

`SpectralKurtosis` — Option to extract spectral kurtosis
`false` (default) | `true`

Option to extract the spectral kurtosis of the time-frequency signal representation, specified as true or false.

If you specify SpectralKurtosis as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the spectral kurtosis feature, see spectralKurtosis.

Data Types: logical

`SpectralSkewness` — Option to extract spectral skewness
`false` (default) | `true`

Option to extract the spectral skewness of the time-frequency signal representation, specified as true or false.

If you specify SpectralSkewness as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the spectral skewness feature, see spectralSkewness.

Data Types: logical

`SpectralCrest` — Option to extract spectral crest
`false` (default) | `true`

Option to extract the spectral crest of the time-frequency signal representation, specified as true or false.

If you specify SpectralCrest as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the spectral crest feature, see spectralCrest.

Data Types: logical

`SpectralFlatness` — Option to extract spectral flatness
`false` (default) | `true`

Option to extract the spectral flatness of the time-frequency signal representation, specified as true or false.

If you specify SpectralFlatness as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the spectral flatness feature, see spectralFlatness.

Data Types: logical

`SpectralEntropy` — Option to extract spectral entropy
`false` (default) | `true`

Option to extract the spectral entropy of the time-frequency signal representation, specified as true or false.

If you specify SpectralEntropy as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the spectral entropy feature, see spectralEntropy.

Data Types: logical

`TFRidges` — Option to extract time-frequency ridges
`false` (default) | `true`

Option to extract the time-frequency ridges of the time-frequency signal representation, specified as true or false.

If you specify TFRidges as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the time-frequency ridge feature, see tfridge.

Data Types: logical

`InstantaneousBandwidth` — Option to extract instantaneous bandwidth
`false` (default) | `true`

Option to extract the instantaneous bandwidth of the time-frequency signal representation, specified as true or false.

If you specify InstantaneousBandwidth as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the instantaneous bandwidth feature, see instbw.

Data Types: logical

`InstantaneousFrequency` — Option to extract instantaneous frequency
`false` (default) | `true`

Option to extract the instantaneous frequency of the time-frequency signal representation, specified as true or false.

If you specify InstantaneousFrequency as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the instantaneous frequency feature, see instfreq or hht.

Data Types: logical

`InstantaneousEnergy` — Option to extract instantaneous energy
`false` (default) | `true`

Option to extract the instantaneous energy of the time-frequency signal representation, specified as true or false.

If you specify InstantaneousEnergy as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the instantaneous energy feature, see hht.

Data Types: logical

`MeanEnvelopeEnergy` — Option to extract mean envelope energy
`false` (default) | `true`

Option to extract the mean energy of the upper and lower envelopes for each intrinsic mode function (IMF) of the time-frequency signal representation, specified as true or false.

If you specify MeanEnvelopeEnergy as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the mean-envelope energy feature, see emd.

Data Types: logical

`WaveletEntropy` — Option to extract wavelet entropy
`false` (default) | `true`

Option to extract the wavelet entropy of the time-frequency signal representation, specified as true or false.

If you specify WaveletEntropy as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the wavelet entropy feature, see wentropy (Wavelet Toolbox).

Data Types: logical

`TimeSpectrum` — Option to extract time-averaged wavelet spectrum
`false` (default) | `true`

Option to extract the time-averaged wavelet spectrum of the time-frequency signal representation, specified as true or false.

If you specify TimeSpectrum as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the time spectrum feature, see timeSpectrum (Wavelet Toolbox).

Data Types: logical

`ScaleSpectrum` — Option to extract scale-averaged wavelet spectrum
`false` (default) | `true`

Option to extract the scale-averaged wavelet spectrum of the time-frequency signal representation, specified as true or false.

If you specify ScaleSpectrum as true:

The signalTimeFrequencyFeatureExtractor object enables this feature for extraction.
The extract object function extracts this feature and concatenates it with all the other features that you enable in the signalTimeFrequencyFeatureExtractor object.

For more information about the scale spectrum feature, see scaleSpectrum (Wavelet Toolbox).

Data Types: logical

Object Functions

`extract`	Extract time-domain, frequency-domain, or time-frequency-domain features
`generateMATLABFunction`	Create MATLAB function compatible with C/C++ code generation
`getExtractorParameters`	Get current parameter values of feature extractor object
`getScalarizationMethods`	Get current scalarization methods of feature extractor object
`setExtractorParameters`	Set nondefault parameter values for feature extractor object
`setScalarizationMethods`	Set scalarization methods for feature extractor object

Examples

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Extract Time-Frequency Features from Chirp Signal

Open Live Script

Extract the spectral kurtosis and instantaneous frequency from the spectrogram of a quadratic-swept chirp signal.

Generate a chirp with quadratic instantaneous frequency deviation. The chirp is sampled at 2 $π$ Hz for 60 seconds. The instantaneous frequency is 0.5 Hz at t = 0 and crosses 2 Hz at t = 60 seconds.

fs = 2*pi;
t = 0:1/fs:60;
x = chirp(t,0.5,60,2,"quadratic");

Create a signalTimeFrequencyFeatureExtractor object and enable the SpectralKurtosis and InstantaneousFrequency time-frequency-domain features.

tfFE = signalTimeFrequencyFeatureExtractor( ...
    SpectralKurtosis=true,InstantaneousFrequency=true)

tfFE = 
  signalTimeFrequencyFeatureExtractor with properties:

   Properties
              FrameSize: []
              FrameRate: []
             SampleRate: []
    IncompleteFrameRule: "drop"
          FeatureFormat: "matrix"
              Transform: "Spectrogram"
    ScalarizationMethod: [1x1 timeFrequencyScalarFeatureOptions]

   Enabled Features
     SpectralKurtosis, InstantaneousFrequency

   Disabled Features
     SpectralSkewness, SpectralCrest, SpectralFlatness, SpectralEntropy, TFRidges, InstantaneousBandwidth
     MeanEnvelopeEnergy, InstantaneousEnergy, WaveletEntropy, TimeSpectrum, ScaleSpectrum

Extract the features from the chirp signal. Reshape the result into the number of features extracted. Plot the features.

features = extract(tfFE,x);

featuresRows = reshape(features,[],2);
stackedplot(featuresRows,"*",...
    DisplayLabels=["Spectral Kurtosis" "Instantaneous Frequency"])
grid on

Figure contains an object of type stackedplot.

Extract Time-Frequency Features from `signalDatastore` Object

Open Live Script

Set up a three-signal signalDatastore object and a signalTimeFrequencyFeatureExtractor object. Extract the spectral skewness and time-frequency ridges to the signals.

Create a signalDatastore object with three oscillating signals sampled at 3000 Hz for 3 seconds.

Fs = 5000;
t = 0:1/Fs:3;
members = {chirp(t,300,t(end),800); ...
        2*chirp(t,200,t(end),1000,"quadratic",[],"concave"); ...
        vco(sin(2*pi*t),[0.1 0.4]*Fs,Fs)};
sds = signalDatastore(members,SampleRate=Fs,OutputDataType="single");

Create a signalTimeFrequencyFeatureExtractor object defining a sample rate. Enable the spectral skewness and time-frequency ridges as features to extract.

tfFE = signalTimeFrequencyFeatureExtractor(SampleRate=Fs, ...
              SpectralSkewness=true,TFRidges=true);

Set up the extractor parameters for a signalTimeFrequencyFeatureExtractor object. Specify the range between Fs/5 and Fs/2.5 to extract the spectral skewness. Set the penalty parameter to zero to extract the time-frequency ridges.

setExtractorParameters(tfFE,"SpectralSkewness",Range=[Fs/5 Fs/2.5]);
setExtractorParameters(tfFE,"TFRidges",Penalty=0);

Extract and plot the time-frequency features to all the signals from a signalDatastore object.

[features,infofeatures] = extract(tfFE,sds);
featureMatrix = cell2mat(features);
featureMatrix(abs(featureMatrix) < 2e-4) = 0;

spectralSkewness = featureMatrix(:,infofeatures{1}.SpectralSkewness);
TFRidges = featureMatrix(:,infofeatures{1}.TFRidges);
featureNames = ["Spectral Skewness" "Time-Frequency Ridges"];

tiledlayout(numel(members),2)
for id=1:numel(members)
    featureValues = {spectralSkewness(id,:) TFRidges(id,:)};
    for fid = 1:numel(featureNames)
        nexttile
        plot(featureValues{fid})
        title(featureNames(fid))
        subtitle("Signal " + id)
        axis tight
    end
end

Feature Vectors and Scalars in Time-Frequency Domain

Open Live Script

Specify scalarization methods to extract the scalar impulse factor and peak value of the instantaneous energy feature for an oscillating signal.

Set the impulse factor and the peak value as scalarization methods for the instantaneous energy time-frequency domain feature.

opts = scalarFeatureOptions("timefrequency", ...
    InstantaneousEnergy={'ImpulseFactor';'PeakValue'})

opts = 
  timeFrequencyScalarFeatureOptions with properties:

          SpectralKurtosis: [0x0 string]
          SpectralSkewness: [0x0 string]
             SpectralCrest: [0x0 string]
          SpectralFlatness: [0x0 string]
           SpectralEntropy: [0x0 string]
                  TFRidges: [0x0 string]
    InstantaneousBandwidth: [0x0 string]
    InstantaneousFrequency: [0x0 string]
       InstantaneousEnergy: [2x1 string]
        MeanEnvelopeEnergy: [0x0 string]
              TimeSpectrum: [0x0 string]
            WaveletEntropy: [0x0 string]
             ScaleSpectrum: [0x0 string]
                       All: [0x0 string]

Create a signalTimeFrequencyFeatureExtractor object that returns scalar values for the instantaneous energy feature from the variational mode decomposition of a signal.

tfFE = signalTimeFrequencyFeatureExtractor(Transform="vmd", ...
    InstantaneousEnergy=true,ScalarizationMethod=opts);

Extract the vector and scalar features for a voltage-controlled oscillating signal.

fs = 10000;
t = 0:1/fs:2;
x = vco(sawtooth(2*pi*t,0.75),[0.1 0.4]*fs,fs);
[features,info] = extract(tfFE,x);

Display the scalar feature values.

% Impulse Factor of the Instantaneous Energy Vector
features(info.InstantaneousEnergyImpulseFactor)

ans = 
8.7551

% Peak Value of the Instantaneous Energy Vector
features(info.InstantaneousEnergyPeakValue)

ans = 
1.3385

Time-Frequency Ridges Feature Extraction from Signal Spectrogram

Open Live Script

Extract the time-frequency ridges from a spectrogram using a signalTimeFrequencyFeatureExtractor object.

Load a data file containing an echolocation pulse batsignal, emitted by a big brown bat (Eptesicus fuscus) and measured with a sample rate DT of 7 microseconds. For more information, see the example Find and Track Ridges Using Reassigned Spectrogram.

load batsignal

Create a MATLAB® timetable using the signal and the time information.

t = (0:length(batsignal)-1)*DT;
sg = timetable(seconds(t)',batsignal);

Create an object to extract time-frequency ridges from the spectrogram of a signal.

fs = 1/DT;
sTFFE = signalTimeFrequencyFeatureExtractor( ...
    Transform="spectrogram",SampleRate=fs,TFRidges=true);

Set the leakage, the time resolution, and the overlap percent parameters for the spectrogram analysis method.

lk = 0.9;
tRes = 0.00028; % seconds
oPercent = 85;

setExtractorParameters(sTFFE,"spectrogram", ...
    Leakage=lk,TimeResolution=tRes,OverlapPercent=oPercent);

Set the number of ridges, number of frequency bins, and penalty parameters for the time-frequency ridges feature.

nRidges = 3;

setExtractorParameters(sTFFE,"TFRidges", ...
  NumRidges=nRidges,NumFrequencyBins=128,Penalty=0.01);

Extract and plot the time-frequency ridges.

tfRidges = reshape(extract(sTFFE,sg),[],nRidges);

tStamps = seconds(tRes/2+(1-oPercent/100)*tRes*(0:length(tfRidges)-1));
plot(tStamps,tfRidges/1000)
xlabel("Time")
ylabel("Frequency (kHz)")

Figure contains an axes object. The axes object with xlabel Time, ylabel Frequency (kHz) contains 3 objects of type line.

Extract Wavelet-Related Time-Frequency Features From Multichannel Signal

This example uses:

Open Live Script

Extract the instantaneous frequency of a signal using the wavelet synchrosqueezed transform (WSST), the time-averaged wavelet spectrum using the continuous wavelet transform (CWT), and the wavelet entropy using the maximal overlap discrete wavelet transform (MODWT).

Load an ECG signal corresponding to record 200 of the MIT-BIH Arrhythmia Database [1]. The workspace variable ecgsig contains the signal, and the variable tm contains the sample times. The sample rate is approximately 400 Hz.

load mit200
Fs = 400;

The signal has 10,000 samples. Reshape the signal into a 1000-by-10 matrix. Each column represents one channel in a multichannel signal. Create a signalDatastore object from the matrix.

ecgsig = reshape(ecgsig,[],10);
ecgsig = num2cell(ecgsig,1);
sds = signalDatastore(ecgsig,SampleRate=Fs);

Set the mean and standard deviation as scalarization methods for the wavelet entropy feature.

opts = scalarFeatureOptions("timefrequency", ...
    WaveletEntropy=["Mean" "StandardDeviation"]);

Create a signalTimeFrequencyFeatureExtractor object that returns:

Wavelet entropy and the scalar values specified in opts
Instantaneous frequency from a WSST
Time-averaged spectrum from a CWT

You can extract instantaneous frequency from time-frequency signal representations created using methods such as the short-time Fourier transform ("spectrogram"), empirical mode decomposition ("emd"), and the maximal overlap discrete wavelet packet transform ("waveletpacket"). Set the time-frequency analysis method to "synchrosqueezedscalogram". The CWT is the only analysis method you can use to extract the time-averaged wavelet spectrum. The signalTimeFrequencyFeatureExtractor object uses the MODWT, the default analysis method of the wentropy function, to extract the wavelet entropy.

tfFE = signalTimeFrequencyFeatureExtractor( ...
    SampleRate=Fs, ...
    Transform="synchrosqueezedscalogram", ...
    InstantaneousFrequency=true, ...
    WaveletEntropy=true, ...
    TimeSpectrum=true, ...
    ScalarizationMethod=opts);

Extract the features from all the signals.

data = read(sds);
[features,info] = extract(tfFE,sds);
featureMatrix = cell2mat(features);

instFrq = featureMatrix(:,info{1}.InstantaneousFrequency);
timeSpect = featureMatrix(:,info{1}.TimeSpectrum);
waveEnt = featureMatrix(:,info{1}.WaveletEntropy);
waveEntMean = featureMatrix(:,info{1}.WaveletEntropyMean);
waveEntStd = featureMatrix(:,info{1}.WaveletEntropyStandardDeviation);

For each signal, compute the mean and standard deviation directly from the extracted wavelet entropy. Confirm they are equal to the extracted scalar values.

[mean(waveEnt,2) waveEntMean]

ans = 10×2

    0.7675    0.7675
    0.7941    0.7941
    0.7533    0.7533
    0.7465    0.7465
    0.8055    0.8055
    0.8141    0.8141
    0.8048    0.8048
    0.7833    0.7833
    0.7999    0.7999
    0.7910    0.7910

[std(waveEnt,0,2) waveEntStd]

ans = 10×2

    0.1714    0.1714
    0.1258    0.1258
    0.2063    0.2063
    0.1945    0.1945
    0.1367    0.1367
    0.1123    0.1123
    0.1235    0.1235
    0.1228    0.1228
    0.1123    0.1123
    0.1168    0.1168

Display the time-averaged wavelet spectrum of the signals as a waterfall plot.

waterfall(timeSpect)
title("Time-Averaged Wavelet Spectrum")
xlabel("Frequency Index")
ylabel("Channel Index")
zlabel("Power")

Figure contains an axes object. The axes object with title Time-Averaged Wavelet Spectrum, xlabel Frequency Index, ylabel Channel Index contains an object of type patch.

Display the instantaneous frequency of the signals as a waterfall plot.

waterfall(instFrq)
view(30,40)
title("Instantaneous Frequency")
xlabel("Sample")
ylabel("Channel Index")
zlabel("Frequency")

Figure contains an axes object. The axes object with title Instantaneous Frequency, xlabel Sample, ylabel Channel Index contains an object of type patch.

More About

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Scalarization Methods for Domain-Specific Signal Features

To set the scalarization methods for features in time domain, frequency domain, or time-frequency domain, select the domain-specific feature extractor objects and scalarization method specification. Refer to the following table for the list of domain-specific features from which you can extract scalar features.

Feature domain	Feature extractor object	Scalarization method specification	Features that support scalarization
Time	`signalTimeFeatureExtractor`	`timeScalarFeatureOptions` object	`PeakValue`
Frequency	`signalFrequencyFeatureExtractor`	`frequencyScalarFeatureOptions` object	`PeakAmplitude` `WelchPSD`
Time-frequency	`signalTimeFrequencyFeatureExtractor`	`timeFrequencyScalarFeatureOptions` object	All time-frequency features

For a given feature vector v with N elements, the scalarization method options convert v to a scalar s as follows.

All Signal Domains

"Mean" — Mean, defined as the average value of v.

$s = \bar{v} = \frac{1}{N} \sum_{i = 1}^{N} v_{i}$
"StandardDeviation" — Standard deviation of the elements of v, normalized by N-1.

$s = \sqrt{\frac{1}{N - 1} \sum_{i = 1}^{N} | v_{i} - \bar{v} |^{2}}$
"PeakValue" — Peak value, defined as the maximum absolute value of v.

$s = v_{p} = \max_{i} | v_{i} |$
"Kurtosis" — Kurtosis, defined as the ratio between the fourth moment of v and the squared second moment of v.

$s = \frac{\frac{1}{N} \sum_{i = 1}^{N} {(v_{i} - \bar{v})}^{4}}{{[\frac{1}{N} \sum_{i = 1}^{N} {(v_{i} - \bar{v})}^{2}]}^{2}}$
"Skewness" — Skewness, defined as the ratio between the third moment of v and the second moment of v raised to the power of 1.5.

$s = \frac{\frac{1}{N} \sum_{i = 1}^{N} {(v_{i} - \bar{v})}^{3}}{{[\frac{1}{N} \sum_{i = 1}^{N} {(v_{i} - \bar{v})}^{2}]}^{3 / 2}}$

Frequency and Time-Frequency Signal Domains

"ClearanceFactor" — Clearance factor, defined as the ratio between the peak value of v and the squared mean of the square roots of the absolute values of v.

$s = \frac{v_{p}}{{(\frac{1}{N} \sum_{i = 1}^{N} \sqrt{| v_{i} |})}^{2}}$
"CrestFactor" — Crest factor, defined as the ratio between the peak value of v and the root-mean-square value of v.

$s = \frac{v_{p}}{\sqrt{\frac{1}{N} \sum_{i = 1}^{N} v_{i}^{2}}}$
"Energy" — Energy, defined as the sum of the squared values of v.

$s = \sum_{i = 1}^{N} v_{i}^{2}$
"Entropy" — Entropy, defined as the sum of plog₂p values, where p is the vector of normalized squared values of v with respect to their sum.
$s = \sum_{i = 1}^{N} p_{i} \log_{2} p_{i},$
where
$p = \frac{v^{2}}{\sum_{i = 1}^{N} v_{i}^{2}} .$
Note
The scalarization method "Entropy" is not supported for the WaveletEntropy nor the SpectralEntropy features.
"ImpulseFactor" — Impulse factor, defined as the ratio between the peak value of v and the average absolute value of v.

$s = \frac{v_{p}}{\frac{1}{N} \sum_{i = 1}^{N} | v_{i} |}$

References

[1] Moody, G.B., and R.G. Mark. “The Impact of the MIT-BIH Arrhythmia Database.” IEEE Engineering in Medicine and Biology Magazine 20, no. 3 (June 2001): 45–50. https://doi.org/10.1109/51.932724.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

You cannot generate code directly from signalTimeFrequencyFeatureExtractor. You can generate C/C++ code from the function returned by generateMATLABFunction.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The signalTimeFrequencyFeatureExtractor function supports GPU array input with these usage notes and limitations:

The "emd" and "vmd" time-frequency analysis methods are not supported.
The MeanEnvelopeEnergy property is not supported.
The InstantaneousEnergy property is supported only if you specify Transform as "wavelet" or "waveletpacket".

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2024a

expand all

R2024b: Use `gpuArray` objects

The signalTimeFrequencyFeatureExtractor object supports gpuArray objects. You must have Parallel Computing Toolbox™ to use this functionality.

signalTimeFrequencyFeatureExtractor

Description

Creation

Syntax

Description

Properties

Main Properties

FrameSize — Number of samples per frame positive integer

FrameRate — Number of samples between start of frames positive integer

FrameOverlapLength — Number of overlapping samples between consecutive frames positive integer

SampleRate — Sample rate [] (default) | positive scalar

FeatureFormat — Format of generated signal features "matrix" (default) | "table"

IncompleteFrameRule — Rule to handle incomplete frames "drop" (default) | "zeropad"

Transform — Time-frequency analysis method "spectrogram" (default) | "synchrosqueezedspectrogram" | "emd" | "vmd" | "scalogram" | "synchrosqueezedscalogram" | "wavelet" | "waveletpacket"

ScalarizationMethod — Methods to convert feature vectors to scalar values timeFrequencyScalarFeatureOptions object

Features to Extract

SpectralKurtosis — Option to extract spectral kurtosis false (default) | true

SpectralSkewness — Option to extract spectral skewness false (default) | true

SpectralCrest — Option to extract spectral crest false (default) | true

SpectralFlatness — Option to extract spectral flatness false (default) | true

SpectralEntropy — Option to extract spectral entropy false (default) | true

TFRidges — Option to extract time-frequency ridges false (default) | true

InstantaneousBandwidth — Option to extract instantaneous bandwidth false (default) | true

InstantaneousFrequency — Option to extract instantaneous frequency false (default) | true

InstantaneousEnergy — Option to extract instantaneous energy false (default) | true

MeanEnvelopeEnergy — Option to extract mean envelope energy false (default) | true

WaveletEntropy — Option to extract wavelet entropy false (default) | true

TimeSpectrum — Option to extract time-averaged wavelet spectrum false (default) | true

ScaleSpectrum — Option to extract scale-averaged wavelet spectrum false (default) | true

Object Functions

Examples

Extract Time-Frequency Features from Chirp Signal

Extract Time-Frequency Features from signalDatastore Object

Feature Vectors and Scalars in Time-Frequency Domain

Time-Frequency Ridges Feature Extraction from Signal Spectrogram

Extract Wavelet-Related Time-Frequency Features From Multichannel Signal

More About

Scalarization Methods for Domain-Specific Signal Features

References

Extended Capabilities

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Version History

R2024b: Use gpuArray objects

See Also

Functions

Objects

Topics

`FrameSize` — Number of samples per frame
positive integer

`FrameRate` — Number of samples between start of frames
positive integer

`FrameOverlapLength` — Number of overlapping samples between consecutive frames
positive integer

`SampleRate` — Sample rate
`[]` (default) | positive scalar

`FeatureFormat` — Format of generated signal features
`"matrix"` (default) | `"table"`

`IncompleteFrameRule` — Rule to handle incomplete frames
`"drop"` (default) | `"zeropad"`

`Transform` — Time-frequency analysis method
`"spectrogram"` (default) | `"synchrosqueezedspectrogram"` | `"emd"` | `"vmd"` | `"scalogram"` | `"synchrosqueezedscalogram"` | `"wavelet"` | `"waveletpacket"`

`ScalarizationMethod` — Methods to convert feature vectors to scalar values
`timeFrequencyScalarFeatureOptions` object

`SpectralKurtosis` — Option to extract spectral kurtosis
`false` (default) | `true`

`SpectralSkewness` — Option to extract spectral skewness
`false` (default) | `true`

`SpectralCrest` — Option to extract spectral crest
`false` (default) | `true`

`SpectralFlatness` — Option to extract spectral flatness
`false` (default) | `true`

`SpectralEntropy` — Option to extract spectral entropy
`false` (default) | `true`

`TFRidges` — Option to extract time-frequency ridges
`false` (default) | `true`

`InstantaneousBandwidth` — Option to extract instantaneous bandwidth
`false` (default) | `true`

`InstantaneousFrequency` — Option to extract instantaneous frequency
`false` (default) | `true`

`InstantaneousEnergy` — Option to extract instantaneous energy
`false` (default) | `true`

`MeanEnvelopeEnergy` — Option to extract mean envelope energy
`false` (default) | `true`

`WaveletEntropy` — Option to extract wavelet entropy
`false` (default) | `true`

`TimeSpectrum` — Option to extract time-averaged wavelet spectrum
`false` (default) | `true`

`ScaleSpectrum` — Option to extract scale-averaged wavelet spectrum
`false` (default) | `true`

Extract Time-Frequency Features from `signalDatastore` Object

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

R2024b: Use `gpuArray` objects