scalarFeatureOptions

Store information for converting feature vectors to scalar values

Since R2024a

Syntax

opts = scalarFeatureOptions

opts = scalarFeatureOptions(domain)

opts = scalarFeatureOptions(domain,Name=Value)

Description

opts = scalarFeatureOptions creates an object opts 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.

opts = scalarFeatureOptions(domain) creates an object opts that stores the scalarization methods for all the signal features from a domain representation with default values.

opts = scalarFeatureOptions(domain,Name=Value) specifies scalarization methods for each signal feature in the signal domain using name-value arguments.

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, or All.

• Valid scalarization method names are ClearanceFactor, Mean, CrestFactor, PeakValue, ImpulseFactor, Energy, StandardDeviation, Kurtosis, Skewness, or Entropy. 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.

  $$s=\overline{v}=\frac{1}{N}{\displaystyle \sum _{i=1}^N{v}_i}$$

• 'StandardDeviation' — Standard deviation of the elements of v, normalized by N-1.

  $$s=\sqrt{\frac{1}{N-1}{\displaystyle \sum _{i=1}^N|v_i-\overline{v}{|}^2}}$$

• 'PeakValue' — Peak value, defined as the maximum absolute value of v.

  $$s=v_p=\underset{i}{\max }\left|v_i\right|$$

• '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.

  $$s=\frac{v_p}{{\left(\frac{1}{N}{\displaystyle \sum _{i=1}^N\sqrt{\left|v_i\right|}}\right)}^2}$$

• 'CrestFactor' — Crest factor, defined as the ratio between the peak value of v and the root-mean-square value of v.

  $$s=\frac{v_p}{\sqrt{\frac{1}{N}{\displaystyle \sum _{i=1}^N{v}_i{}^2}}}$$

• 'Energy' — Energy, defined as the sum of the squared values of v.

  $$s={\displaystyle \sum _{i=1}^N{v}_i{}^2}$$

• '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.

  $$s={\displaystyle \sum _{i=1}^N{p}_i{\log }_2{p}_i},$$

  where

  $$p=\frac{v^2}{{\displaystyle \sum _{i=1}^N{v}_i{}^2}}.$$

  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.

  $$s=\frac{v_p}{\frac{1}{N}{\displaystyle \sum _{i=1}^N\left|v_i\right|}}$$

• 'Kurtosis' — Kurtosis, defined as the ratio between the fourth moment of v and the squared second moment of v.

  $$s=\frac{\frac{1}{N}{\displaystyle \sum _{i=1}^N{(v_i-\overline{v})}^4}}{{\left[\frac{1}{N}{\displaystyle \sum _{i=1}^N{(v_i-\overline{v})}^2}\right]}^2}$$

• '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.

  $$s=\frac{\frac{1}{N}{\displaystyle \sum _{i=1}^N{(v_i-\overline{v})}^3}}{{\left[\frac{1}{N}{\displaystyle \sum _{i=1}^N{(v_i-\overline{v})}^2}\right]}^{3/2}}$$

Version History

Introduced in R2024a