scalarFeatureOptions
Syntax
Description
creates an object
opts
= scalarFeatureOptionsopts
that stores the scalarization methods for all the
time-frequency-domain features with default values. You can use the resulting object to set
the ScalarizationMethod
property of a signalTimeFrequencyFeatureExtractor
object.
specifies scalarization methods for each signal feature in the signal domain using
name-value arguments.opts
= scalarFeatureOptions(domain
,Name=Value
)
Examples
Extract Signal Features in Time-Frequency Domain
Specify the impulse factor and peak value as scalarization methods for the instantaneous frequency feature.
opts = scalarFeatureOptions("timefrequency",... InstantaneousFrequency={'ImpulseFactor';'PeakValue'});
Create a time-frequency feature extractor that returns the desired scalar features using the empirical mode decomposition (EMD) transform.
tfFE = signalTimeFrequencyFeatureExtractor(Transform="emd", ... InstantaneousFrequency=true,ScalarizationMethod=opts);
Extract the instantaneous frequency vector feature and the desired scalar features from the EMD transform of a sinusoidal signal.
x = sin(2*pi/5*(0:1:7)); [features,info] = extract(tfFE,x)
features = 1×10
1.0165 1.0416 1.0887 1.1918 1.2957 1.2502 1.0863 0.9903 1.1567 1.2957
info = struct with fields:
InstantaneousFrequency: [1 2 3 4 5 6 7 8]
InstantaneousFrequencyImpulseFactor: 9
InstantaneousFrequencyPeakValue: 10
Vector and Scalar Features in Time-Frequency Domain
Set scalarization methods and use for converting time-frequency feature vectors to scalar values.
Specify 'Entropy'
and 'Mean'
as scalarization methods for the spectral flatness feature. Specify 'Kurtosis'
as the scalarization method for all the enabled signal features. Store this information in a timeFrequencyScalarFeatureOptions
object.
opts = scalarFeatureOptions("timefrequency", ... SpectralFlatness={'Entropy' 'Mean'}, ... All={'Kurtosis'})
opts = timeFrequencyScalarFeatureOptions with properties: SpectralKurtosis: [0x0 string] SpectralSkewness: [0x0 string] SpectralCrest: [0x0 string] SpectralFlatness: ["Entropy" "Mean"] SpectralEntropy: [0x0 string] TFRidges: [0x0 string] InstantaneousBandwidth: [0x0 string] InstantaneousFrequency: [0x0 string] InstantaneousEnergy: [0x0 string] MeanEnvelopeEnergy: [0x0 string] TimeSpectrum: [0x0 string] WaveletEntropy: [0x0 string] ScaleSpectrum: [0x0 string] All: "Kurtosis"
Create a signalTimeFrequencyFeatureExtractor
object to extract the spectral crest, spectral flatness, and time-frequency ridges features. Use opts
to set the scalarization method property of the feature extractor object.
sFE = signalTimeFrequencyFeatureExtractor( ... SpectralCrest=true,SpectralFlatness=true, ... TFRidges=true,ScalarizationMethod=opts)
sFE = signalTimeFrequencyFeatureExtractor with properties: Properties FrameSize: [] FrameRate: [] SampleRate: [] IncompleteFrameRule: "drop" FeatureFormat: "matrix" Transform: "Spectrogram" ScalarizationMethod: [1x1 timeFrequencyScalarFeatureOptions] Enabled Features SpectralCrest, SpectralFlatness, TFRidges Disabled Features SpectralKurtosis, SpectralSkewness, SpectralEntropy, InstantaneousBandwidth, InstantaneousFrequency, MeanEnvelopeEnergy InstantaneousEnergy, WaveletEntropy, TimeSpectrum, ScaleSpectrum
Extract vectors and scalar features from the spectrogram of a signal. Observe the list of extracted features.
x = exp(-(-4.5:0.15:4.5).^2); [features,indices] = extract(sFE,x); disp(indices)
SpectralCrest: [1 2 3 4 5] SpectralCrestKurtosis: 6 SpectralFlatness: [7 8 9 10 11] SpectralFlatnessEntropy: 12 SpectralFlatnessMean: 13 SpectralFlatnessKurtosis: 14 TFRidges: [15 16 17 18 19] TFRidgesKurtosis: 20
Input Arguments
domain
— Domain of signal representation
"timefrequency"
Domain of signal representation, specified as
"timefrequency"
.
Data Types: char
| string
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: scalarFeatureOptions("timefrequency",SpectralCrest={'Mean';'CrestFactor'})
sets scalarization methods to extract the mean and crest factor from the spectral crest
feature vector of the time-frequency representation of a signal.
When domain
is "timefrequency"
, the name-value
arguments must correspond with signal features in the time-frequency domain.
Valid feature names are
SpectralKurtosis
,SpectralSkewness
,SpectralCrest
,SpectralFlatness
,SpectralEntropy
,TFRidges
,InstantaneousBandwidth
,InstantaneousFrequency
,MeanEnvelopeEnergy
,InstantaneousEnergy
,WaveletEntropy
,TimeSpectrum
,ScaleSpectrum
, orAll
.Valid scalarization method names are
ClearanceFactor
,Mean
,CrestFactor
,PeakValue
,ImpulseFactor
,Energy
,StandardDeviation
,Kurtosis
,Skewness
, orEntropy
. For more information about scalarization methods, see Scalarization Methods for Signal Features in Time-Frequency Domain.
SpectralKurtosis
— Scalarization methods for spectral kurtosis feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the spectral kurtosis feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the spectral kurtosis feature, see spectralKurtosis
.
Example: SpectralKurtosis={'Mean' 'PeakValue'}
sets the option
to extract the mean and the peak value of the spectral kurtosis feature
vector.
Data Types: cell
| string
SpectralSkewness
— Scalarization methods for spectral skewness feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the spectral skewness feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the spectral skewness feature, see spectralSkewness
.
Example: SpectralSkewness={'Mean' 'PeakValue'}
sets the option
to extract the mean and the peak value of the spectral skewness feature
vector.
Data Types: cell
| string
SpectralCrest
— Scalarization methods for spectral crest feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the spectral crest feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the spectral crest feature, see spectralCrest
.
Example: SpectralCrest={'Mean' 'PeakValue'}
sets the option to
extract the mean and the peak value of the spectral crest feature
vector.
Data Types: cell
| string
SpectralFlatness
— Scalarization methods for spectral flatness feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the spectral flatness feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the spectral flatness feature, see spectralFlatness
.
Example: SpectralFlatness={'Mean' 'PeakValue'}
sets the option
to extract the mean and the peak value of the spectral flatness feature
vector.
Data Types: cell
| string
SpectralEntropy
— Scalarization methods for spectral entropy feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the spectral entropy feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the spectral entropy feature, see spectralEntropy
.
Example: SpectralEntropy={'Mean' 'PeakValue'}
sets the option to
extract the mean and the peak value of the spectral entropy feature
vector.
Data Types: cell
| string
TFRidges
— Scalarization methods for time-frequency ridges feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the time-frequency ridges feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the time-frequency ridge feature, see tfridge
.
Example: TFRidges={'Mean' 'PeakValue'}
sets the option to
extract the mean and the peak value of the time-frequency ridge feature
vector.
Data Types: cell
| string
InstantaneousBandwidth
— Scalarization methods for instantaneous bandwidth feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the instantaneous bandwidth feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the instantaneous bandwidth feature, see instbw
.
Example: InstantaneousBandwidth={'Mean' 'PeakValue'}
sets the
option to extract the mean and the peak value of the instantaneous bandwidth feature
vector.
Data Types: cell
| string
InstantaneousFrequency
— Scalarization methods for instantaneous frequency feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the instantaneous frequency feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the instantaneous frequency feature, see instfreq
or hht
.
Example: InstantaneousFrequency={'Mean' 'PeakValue'}
sets the
option to extract the mean and the peak value of the instantaneous frequency feature
vector.
Data Types: cell
| string
InstantaneousEnergy
— Scalarization methods for instantaneous energy feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the instantaneous energy feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the instantaneous energy feature, see hht
.
Example: InstantaneousEnergy={'Mean' 'PeakValue'}
sets the
option to extract the mean and the peak value of the instantaneous energy feature
vector.
Data Types: cell
| string
MeanEnvelopeEnergy
— Scalarization methods for mean envelope energy feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the mean envelope energy feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the mean envelope energy feature, see emd
.
Example: MeanEnvelopeEnergy={'Mean' 'PeakValue'}
sets the option
to extract the mean and the peak value of the mean envelope energy feature
vector.
Data Types: cell
| string
WaveletEntropy
— Scalarization methods for wavelet entropy feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the wavelet entropy feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the wavelet entropy feature, see wentropy
(Wavelet Toolbox).
Example: WaveletEntropy={'Mean' 'PeakValue'}
sets the option to
extract the mean and the peak value of the wavelet entropy feature
vector.
Data Types: cell
| string
TimeSpectrum
— Scalarization methods for time-averaged wavelet spectrum feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the time-averaged wavelet spectrum feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the time spectrum feature, see timeSpectrum
(Wavelet Toolbox).
Example: TimeSpectrum={'Mean' 'PeakValue'}
sets the option to
extract the mean and the peak value of the time spectrum feature
vector.
Data Types: cell
| string
ScaleSpectrum
— Scalarization methods for scale-averaged wavelet spectrum feature vector
strings(0)
(default) | cell array | string array
Scalarization methods for the scale-averaged wavelet spectrum feature, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
the feature, if enabled. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify this feature, the feature extractor object:
Associates with it any scalarization methods you have specified for it.
Appends any scalarization methods you have specified using the
All
name-value argument.
For more information about the scale spectrum feature, see scaleSpectrum
(Wavelet Toolbox).
Example: ScaleSpectrum={'Mean' 'PeakValue'}
sets the option to
extract the mean and the peak value of the scale spectrum feature
vector.
Data Types: cell
| string
All
— Scalarization methods for all signal feature
strings(0)
(default) | cell array | string array
Scalarization methods for all the signal features, specified as a cell array of character vectors or as a string array.
Each element of the array corresponds to a scalarization method that you apply on
all the enabled features. Enable signal features for extraction when creating the
signalTimeFrequencyFeatureExtractor
object.
If you specify All
, the feature extractor object:
Associates the scalarization methods you have specified with all enabled features.
Appends them to the list of methods already specified for each particular feature.
For more information about signal features in the time-frequency domain, see
signalTimeFrequencyFeatureExtractor
.
Example: All={'Mean' 'PeakValue'}
sets the option to extract the
mean and the peak value of all the feature vectors.
Data Types: cell
| string
Output Arguments
opts
— List of features and scalarization methods
timeFrequencyScalarFeatureOptions
object
List of features and their corresponding scalarization methods, returned as a
timeFrequencyScalarFeatureOptions
object.
By default, the scalarFeatureOptions
function sets the scalarization
method for each feature to an empty string array. In this case, the extract
function of the feature extractor returns the feature vectors without converting them to
scalar values.
More About
Scalarization Methods for Signal Features in Time-Frequency Domain
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.'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.'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.
Version History
Introduced in R2024a
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