signalTimeFrequencyFeatureExtractor
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
Description
creates a sFE
= signalTimeFrequencyFeatureExtractorsignalTimeFrequencyFeatureExtractor
object with default property values.
sets property values to the sFE
= signalTimeFrequencyFeatureExtractor(Name=Value
)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.
Properties
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 | ||||
---|---|---|---|---|---|
"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
, orFrameOverlapLength
whenTransform
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
, thesignalTimeFrequencyFeatureExtractor
object 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 totrue
.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
, thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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 thesignalTimeFrequencyFeatureExtractor
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
Extract Time-Frequency Features from Chirp Signal
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 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
Extract Time-Frequency Features from signalDatastore
Object
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
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
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)")
Extract Wavelet-Related Time-Frequency Features From Multichannel Signal
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")
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")
More About
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.
"Mean"
— Mean, defined as the average value of v."StandardDeviation"
— Standard deviation of the elements of v, normalized by N-1."PeakValue"
— Peak value, defined as the maximum absolute value of v."Kurtosis"
— Kurtosis, defined as the ratio between the fourth moment of v and the squared second moment of v."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.
"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."CrestFactor"
— Crest factor, defined as the ratio between the peak value of v and the root-mean-square value of v."Energy"
— Energy, defined as the sum of the squared values of v."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.where
Note
The scalarization method
"Entropy"
is not supported for theWaveletEntropy
nor theSpectralEntropy
features."ImpulseFactor"
— Impulse factor, defined as the ratio between the peak value of v and the average absolute value of v.
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 bygenerateMATLABFunction
.
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 specifyTransform
as"wavelet"
or"waveletpacket"
.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2024aR2024b: Use gpuArray
objects
The signalTimeFrequencyFeatureExtractor
object supports gpuArray
objects.
You must have Parallel Computing Toolbox™ to use this functionality.
See Also
Functions
Objects
MATLAB 命令
您点击的链接对应于以下 MATLAB 命令:
请在 MATLAB 命令行窗口中直接输入以执行命令。Web 浏览器不支持 MATLAB 命令。
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)