signalFrequencyFeatureExtractor
Description
Use signalFrequencyFeatureExtractor
to extract
frequency-domain features from a signal. You can use the extracted features to train a machine
learning model or a deep learning network.
Creation
Description
creates a sFE
= signalFrequencyFeatureExtractorsignalFrequencyFeatureExtractor
object with default property
values.
specifies nondefault property values of the
sFE
= signalFrequencyFeatureExtractor(Name=Value
)signalFrequencyFeatureExtractor
object. For example,
signalFrequencyFeatureExtractor(FrameSize=30,FrameOverlapLength=6)
divides the signal into overlapping 30-sample frames and extracts 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
ScalarizationMethod
— Methods to convert feature vectors to scalar values
frequencyScalarFeatureOptions
object
Since R2024b
Methods to convert feature vectors to scalar values, specified as a frequencyScalarFeatureOptions
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
, thesignalFrequencyFeatureExtractor
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
signalFrequencyFeatureExtractor
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
frequencyScalarFeatureOptions
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
, thesignalFrequencyFeatureExtractor
object does not perform any scalarization.
For more information about scalarization methods, see Scalarization Methods for Domain-Specific Signal Features.
Features to Extract
MeanFrequency
— Option to extract mean frequency
false
(default) | true
Option to extract the mean frequency of the power spectrum, specified as
true
or false
. If you specify
MeanFrequency
as true, the signalFrequencyFeatureExtractor
object
extracts the mean frequency of the power spectrum and appends it to the features
returned by the extract
function.
Data Types: logical
MedianFrequency
— Option to extract median frequency
false
(default) | true
Option to extract the median frequency of the power spectrum, specified as
true
or false
. If you specify
MedianFrequency
as true, the signalFrequencyFeatureExtractor
object extracts the median frequency of the power spectrum and appends it to the
features returned by the extract
function.
Data Types: logical
BandPower
— Option to extract average band power
false
(default) | true
Option to extract the average band power, specified as true
or
false
. If you specify BandPower
as true, the
signalFrequencyFeatureExtractor
object extracts the band power and appends it to the
features returned by the extract
function.
Data Types: logical
OccupiedBandwidth
— Option to extract occupied bandwidth
false
(default) | true
Option to extract the 99% occupied bandwidth, specified as true
or false
. If you specify OccupiedBandwidth
as
true, the signalFrequencyFeatureExtractor
object extracts the 99% occupied bandwidth and
appends it to the features returned by the extract
function.
To set parameters of the occupied bandwidth extraction, use setExtractorParameters
.
setExtractorParameters(sFE,"OccupiedBandwidth",Name=Value)
Settable parameters for the occupied bandwidth extraction are:
Percentage
— Power percentage, specified as a positive integer between0
and100
.
Data Types: logical
PowerBandwidth
— Option to extract half-power bandwidth
false
(default) | true
Option to extract the 3 dB (half-power) bandwidth, specified as
true
or false
. If you specify
PowerBanwidth
as true, the signalFrequencyFeatureExtractor
object
extracts the 3 dB bandwidth value and appends it to the features returned by the
extract
function.
To set parameters of the half-power bandwidth extraction, use setExtractorParameters
.
setExtractorParameters(sFE,"PowerBandwidth",Name=Value)
Settable parameters for the half-power bandwidth extraction are:
RelativeAmplitude
— Relative amplitude, specified as an integer.
Data Types: logical
WelchPSD
— Option to extract power spectral density estimate
false
(default) | true
Option to extract the power spectral density (PSD) estimate, specified as
true
or false
. If you specify
WelchPSD
as true, the signalFrequencyFeatureExtractor
object
extracts the PSD estimate using Welch's method and appends it to the features returned
by the extract
function.
To set parameters of the Welch's PSD estimate, use setExtractorParameters
.
setExtractorParameters(sFE,"WelchPSD",Name=Value)
Settable parameters for the Welch's PSD estimate extraction are:
FFTLength
— Number of DFT points, specified as a positive integer.FrequencyVector
— Frequencies at which the PSD is estimated, specified as a vector with at least two elements. You can specifyFrequencyVector
only whenFFTLength
is not specified.OverlapLength
— Number of overlapping samples, specified as a positive integer.Window
— Window, specified as a scalar or vector.
Data Types: logical
PeakAmplitude
— Option to extract peak amplitude
false
(default) | true
Option to extract the peak spectral amplitudes, specified as
true
or false
. If you specify
PeakAmplitude
as true, the signalFrequencyFeatureExtractor
object
extracts the peak amplitudes of the computed Welch PSD estimate and appends them to
the features returned by the extract
function.
To set parameters of the peak amplitude extraction, use setExtractorParameters
.
setExtractorParameters(sFE,"PeakAmplitude",Name=Value)
Settable parameters for the peak amplitude extraction are:
PeakType
— Type of peak, specified as"minima"
or"maxima"
.MaxNumExtrema
— Maximum number of peaks, specified as a positive integer scalar.MinProminence
— Minimum prominence, specified as a positive scalar. The object returns only peaks whose prominence is at least the value specified.MinSeparation
— Minimum separation between peaks, specified as a positive scalar.FlatSelection
— Flat region indicator, specified as one of these:"center"
— Indicate only the center element of a flat region as the peak."first"
— Indicate only the first element of a flat region as the peak."last"
— Indicate only the last element of a flat region as the peak."all"
— Indicate all elements of a flat region as the peak.
Data Types: logical
PeakLocation
— Option to extract peak location
false
(default) | true
Option to extract the spectral peak locations, specified as
true
or false
. If you specify
PeakLocation
as true, the signalFrequencyFeatureExtractor
object
extracts the peak locations of the computed Welch PSD estimate and appends them to the
features returned by the extract
function.
To set parameters of the peak location extraction, use setExtractorParameters
.
setExtractorParameters(sFE,"PeakLocation",Name=Value)
Settable parameters for the peak location extraction are:
PeakType
— Type of peak, specified as"minima"
or"maxima"
.MaxNumExtrema
— Maximum number of peaks, specified as a positive integer scalar.MinProminence
— Minimum prominence, specified as a positive scalar. ThesetExtractorParameters
function returns only peaks whose prominence is at least the value specified.MinSeparation
— Minimum separation between peaks, specified as a positive scalar.FlatSelection
— Flat region indicator, specified as one of these:"center"
— Indicate only the center element of a flat region as the peak."first"
— Indicate only the first element of a flat region as the peak."last"
— Indicate only the last element of a flat region as the peak."all"
— Indicate all elements of a flat region as the peak.
Data Types: logical
Note
To compute frequency features, signalFrequencyFeatureExtractor
first
estimates the PSD of the input time-domain signal using Welch's method. The object uses
the computed Welch PSD and corresponding frequency vector to compute the specified
features. You can configure the computed Welch PSD estimate using the setExtractorParameters
function.
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 |
setExtractorParameters | Set nondefault parameter values for feature extractor object |
Examples
Extract Frequency-Domain Features From Signal
Generate 1024 samples of a chirp sampled at 1024 kHz. The chirp has an initial frequency of 50 kHz and reaches 100 kHz at the end of the sampling. Add white Gaussian noise such that the signal-to-noise ratio is 40 dB. Plot the power spectral density (PSD) and annotate the mean frequency and occupied bandwidth of the signal.
nSamp = 1024; Fs = 1024e3; SNR = 40; t = (0:nSamp-1)'/Fs; x0 = chirp(t,50e3,nSamp/Fs,100e3); x = x0+randn(size(x0))*std(x0)/db2mag(SNR); [pxx,f]= pwelch(x,[],[],[],Fs); obw(pxx,f); m = meanfreq(pxx,f); xline(m/1000,"--", ... "Mean Frequency Estimate: "+m/1000+" kHz")
Create a signalFrequencyFeatureExtractor
object to extract the mean frequency, 99% occupied bandwidth, and 3 dB bandwidth of the signal.
sFE = signalFrequencyFeatureExtractor(SampleRate=Fs, ...
MeanFrequency=true,OccupiedBandwidth=true,PowerBandwidth=true)
sFE = signalFrequencyFeatureExtractor with properties: Properties FrameSize: [] FrameRate: [] SampleRate: 1024000 IncompleteFrameRule: "drop" FeatureFormat: "matrix" Enabled Features MeanFrequency, OccupiedBandwidth, PowerBandwidth Disabled Features MedianFrequency, BandPower, WelchPSD, PeakAmplitude, PeakLocation
Call the extract
function to extract the specified features.
[features,info] = extract(sFE,x)
features = 1×3
104 ×
7.2252 4.3783 3.7773
info = struct with fields:
MeanFrequency: 1
OccupiedBandwidth: 2
PowerBandwidth: 3
To view the extracted features in a table, modify the FeatureFormat
property of the object.
sFE.FeatureFormat = "table";
featureTable = extract(sFE,x)
featureTable=1×5 table
FrameStartTime FrameEndTime MeanFrequency OccupiedBandwidth PowerBandwidth
______________ ____________ _____________ _________________ ______________
1 1024 72252 43783 37773
You can use the getExtractorParameters
function to view parameters used to compute a specified feature. The occupied bandwidth measures the bandwidth containing 99% of the total power for the input signal by default. Use the setExtractorParameters
function to change the percentage to 95% and extract the specified features again.
params = getExtractorParameters(sFE,"OccupiedBandwidth")
params = struct with fields:
Percentage: []
params.Percentage = 95; setExtractorParameters(sFE,OccupiedBandwidth=params) featuresTable2 = extract(sFE,x)
featuresTable2=1×5 table
FrameStartTime FrameEndTime MeanFrequency OccupiedBandwidth PowerBandwidth
______________ ____________ _____________ _________________ ______________
1 1024 72252 39840 37773
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.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
You cannot generate code directly from
signalFrequencyFeatureExtractor
. 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™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2021bR2024b: Specify scalarization methods for frequency-domain signal features
The signalFrequencyFeatureExtractor
function supports specifying methods to extract
scalar features in frequency domain.
R2023a: Use gpuArray
inputs
The signalFrequencyFeatureExtractor
object supports gpuArray
inputs. You
must have Parallel Computing Toolbox™ to use this functionality.
See Also
Functions
Objects
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