signalLabelDefinition
Create signal label definition
Description
Use signalLabelDefinition
to create signal label definitions for
data sets. The labels can correspond to attributes, regions, or points of interest. Use a
vector of signalLabelDefinition
objects to create a labeledSignalSet
.
Creation
Description
sets Properties using name-value arguments.
You can specify multiple name-value arguments. Enclose each property name in
quotes.sld
= signalLabelDefinition(name
,Name=Value
)
Input Arguments
name
— Label name
character vector | string scalar
Label name, specified as a character vector or string scalar.
Data Types: char
| string
Properties
Name
— Name of label
character vector | string scalar
Name of label, specified as a character vector or string scalar.
Data Types: char
| string
LabelType
— Type of label
"attribute"
(default) | "roi"
| "point"
| "attributeFeature"
| "roiFeature"
Type of label, specified as one of these:
"attribute"
— Define signal characteristics."roi"
— Define signal characteristics over regions of interest."point"
— Define signal characteristics over points of interest."attributeFeature"
— Define signal characteristics that correspond to features."roiFeature"
— Define signal characteristics over regions of interest that correspond to features.
Data Types: char
| string
LabelDataType
— Data type of label
"logical"
(default) | "categorical"
| "numeric"
| "string"
| "table"
| "timetable"
Data type of label, specified as "logical"
,
"categorical"
, "numeric"
,
"string"
, "table"
, or
"timetable"
. When you set this property to
"categorical"
, use the Categories property to specify the array
of categories. The object does not support timetable and table data types for
attributeFeature
and roiFeature
labels.
Data Types: char
| string
Categories
— Label category names
string array | cell array of character vectors
Label category names, specified as a string array or a cell array of character
vectors. The array must have unique elements. This property applies only when the LabelDataType property is set to
"categorical"
.
Example: LabelDataType="categorical",Categories=["apple","orange"]
Data Types: char
| string
ROILimitsDataType
— Data type of ROI limits
"double"
(default) | "duration"
Data type of ROI limits, specified as either "double"
or
"duration"
. This property applies only when LabelType is set to
"roi"
.
Data Types: char
| string
PointLocationsDataType
— Data type of point locations
"double"
(default) | "duration"
Data type of point locations, specified as either "double"
or
"duration"
. This property applies only when LabelType is set to
"point"
.
Data Types: char
| string
ValidationFunction
— Validation function
function handle
Validation function, specified as a function handle and used when setting label
values in a labeledSignalSet
object. This property applies only when
LabelDataType is set to
"categorical"
, "logical"
,
"numeric"
, "table"
, or
"timetable"
. If not specified, the function checks only that its
input values are of the correct data type. If LabelDataType is set to
"categorical"
, the function checks that the input is one of the
values specified using Categories. The function takes an input
value and returns true
if the value is valid and
false
if the value is invalid.
Example: LabelDataType
="numeric",DefaultValue
=1,ValidationFunction
=@(x)x<2
Data Types: function_handle
DefaultValue
— Default value of label
[]
(default) | LabelDataType
value
Default value of label, specified as a value of the type specified using LabelDataType. If LabelDataType is set to
"categorical"
, then DefaultValue must be one of the values
specified using Categories.
Example: LabelDataType
="categorical",Categories
=["apple","orange"],DefaultValue
="apple"
Data Types: char
| double
| logical
| string
| table
Description
— Label description
character vector | string scalar
Label description, specified as a character vector or string scalar.
Example: Description="Patient is asleep"
Data Types: char
| string
Tag
— Label tag identifier
character vector | string scalar
Label tag identifier, specified as a character vector or string scalar. Use this property to identify the same label in a larger labeling scheme or public labeling set.
Example: Tag="Peak1"
Data Types: char
| string
Sublabels
— Array of sublabels
signal label definition object
Array of sublabels, specified as a signal label definition object. To specify more
than one sublabel, set this property to a vector of signal label definition objects. Use
this property to create a relationship between a parent label and its children. If LabelType is set to
"attributeFeature"
or "roiFeature"
, then this
property does not apply.
Note
Sublabels cannot have sublabels.
Example: Sublabels
=[signalLabelDefinition("negative"),signalLabelDefinition("positive")]
FrameSize
— Frame size
numeric scalar
Frame size, specified as a numeric scalar. You must specify
FrameSize
when LabelType is set to
"roiFeature"
.
Example: FrameSize=50
Data Types: double
FrameOverlapLength
— Overlap length of adjacent frames
0
(default) | numeric scalar
Overlap length of adjacent frames, specified as a numeric scalar. To enable this
property, set LabelType to
"roiFeature"
. You cannot specify
FrameOverlapLength
and FrameRate
simultaneously. If you do not specify FramerOverlapLength
, then the
object assumes the overlap length to be zero.
Example: FrameSize=50,FrameOverlapLength=5
Data Types: double
FrameRate
— Frame rate
0
(default) | numeric scalar
Frame rate, specified as a numeric scalar. To enable this property, set LabelType to
"roiFeature"
. You cannot specify FrameRate
and
FrameOverlapLength
simultaneously. If you do not specify
FrameRate
, then the object assumes no overlap between
frames.
Example: FrameSize=50,FrameRate=45
Data Types: double
Object Functions
labelDefinitionsHierarchy | Get hierarchical list of label and sublabel names |
labelDefinitionsSummary | Get summary table of signal label definitions |
Examples
Label Definitions for Whale Songs
Consider a set of whale sound recordings. The recorded whale sounds consist of trills and moans. Trills sound like series of clicks. Moans are low-frequency cries similar to the sound made by a ship's horn. You want to look at each signal and label it to identify the whale type, the trill regions, and the moan regions. For each trill region, you also want to label the signal peaks higher than a certain threshold.
Signal Label Definitions
Define an attribute label to store whale types. The possible categories are blue whale, humpback whale, and white whale.
dWhaleType = signalLabelDefinition('WhaleType',... 'LabelType','attribute',... 'LabelDataType','categorical',... 'Categories',{'blue','humpback','white'},... 'Description','Whale type');
Define a region-of-interest (ROI) label to capture moan regions. Define another ROI label to capture trill regions.
dMoans = signalLabelDefinition('MoanRegions',... 'LabelType','roi',... 'LabelDataType','logical',... 'Description','Regions where moans occur'); dTrills = signalLabelDefinition('TrillRegions',... 'LabelType','roi',... 'LabelDataType','logical',... 'Description','Regions where trills occur');
Finally, define a point label to capture the trill peaks. Set this label as a sublabel of the dTrills
definition.
dTrillPeaks = signalLabelDefinition('TrillPeaks',... 'LabelType','point',... 'LabelDataType','numeric',... 'Description','Trill peaks'); dTrills.Sublabels = dTrillPeaks;
Labeled Signal Set
Create a labeledSignalSet
with the whale signals and the label definitions. Add label values to identify the whale type, the moan and trill regions, and the peaks of the trills.
load labelwhalesignals lbldefs = [dWhaleType dMoans dTrills]; lss = labeledSignalSet({whale1 whale2},lbldefs,'MemberNames',{'Whale1','Whale2'}, ... 'SampleRate',Fs,'Description','Characterize whale song regions');
Visualize the label hierarchy and label properties using labelDefinitionsHierarchy
and labelDefinitionsSummary
.
labelDefinitionsHierarchy(lss)
ans = 'WhaleType Sublabels: [] MoanRegions Sublabels: [] TrillRegions Sublabels: TrillPeaks '
labelDefinitionsSummary(lss)
ans=3×9 table
LabelName LabelType LabelDataType Categories ValidationFunction DefaultValue Sublabels Tag Description
______________ ___________ _____________ ____________ __________________ ____________ ___________________________ ___ ____________________________
"WhaleType" "attribute" "categorical" {3x1 string} {["N/A" ]} {0x0 double} {0x0 double } "" "Whale type"
"MoanRegions" "roi" "logical" {["N/A" ]} {0x0 double} {0x0 double} {0x0 double } "" "Regions where moans occur"
"TrillRegions" "roi" "logical" {["N/A" ]} {0x0 double} {0x0 double} {1x1 signalLabelDefinition} "" "Regions where trills occur"
The signals in the loaded data correspond to songs of two blue whales. Set the 'WhaleType'
values for both signals.
setLabelValue(lss,1,'WhaleType','blue'); setLabelValue(lss,2,'WhaleType','blue');
Visualize the 'Labels'
property. The table has the newly added 'WhaleType'
values for both signals.
lss.Labels
ans=2×3 table
WhaleType MoanRegions TrillRegions
_________ ___________ ____________
Whale1 blue {0x2 table} {0x3 table}
Whale2 blue {0x2 table} {0x3 table}
Visualize Region Labels
Visualize the whale songs to identify the trill and moan regions.
subplot(2,1,1) plot((0:length(whale1)-1)/Fs,whale1) ylabel('Whale 1') subplot(2,1,2) plot((0:length(whale2)-1)/Fs,whale2) ylabel('Whale 2')
Moan regions are sustained low-frequency wails.
whale1
has moans centered at about 7 seconds, 12 seconds, and 17 seconds.whale2
has moans centered at about 3 seconds, 7 seconds, and 16 seconds.
Add the moan regions to the labeled set. Specify the ROI limits in seconds and the label values.
moanRegionsWhale1 = [6.1 7.7; 11.4 13.1; 16.5 18.1]; mrsz1 = [size(moanRegionsWhale1,1) 1]; setLabelValue(lss,1,'MoanRegions',moanRegionsWhale1,true(mrsz1)); moanRegionsWhale2 = [2.5 3.5; 5.8 8; 15.4 16.7]; mrsz2 = [size(moanRegionsWhale2,1) 1]; setLabelValue(lss,2,'MoanRegions',moanRegionsWhale2,true(mrsz2));
Trill regions have distinct bursts of sound punctuated by silence.
whale1
has a trill centered at about 2 seconds.whale2
has a trill centered at about 12 seconds.
Add the trill regions to the labeled set.
trillRegionWhale1 = [1.4 3.1]; trsz1 = [size(trillRegionWhale1,1) 1]; setLabelValue(lss,1,'TrillRegions',trillRegionWhale1,true(trsz1)); trillRegionWhale2 = [11.1 13]; trsz2 = [size(trillRegionWhale1,1) 1]; setLabelValue(lss,2,'TrillRegions',trillRegionWhale2,true(trsz2));
Create a signalMask
object for each whale song and use it to visualize and label the different regions. For better visualization, change the label values from logical to categorical.
mr1 = getLabelValues(lss,1,'MoanRegions'); mr1.Value = categorical(repmat("moan",mrsz1)); tr1 = getLabelValues(lss,1,'TrillRegions'); tr1.Value = categorical(repmat("trill",trsz1)); msk1 = signalMask([mr1;tr1],'SampleRate',Fs); subplot(2,1,1) plotsigroi(msk1,whale1) ylabel('Whale 1') hold on mr2 = getLabelValues(lss,2,'MoanRegions'); mr2.Value = categorical(repmat("moan",mrsz2)); tr2 = getLabelValues(lss,2,'TrillRegions'); tr2.Value = categorical(repmat("trill",trsz2)); msk2 = signalMask([mr2;tr2],'SampleRate',Fs); subplot(2,1,2) plotsigroi(msk2,whale2) ylabel('Whale 2') hold on
Visualize Point Labels
Label three peaks for each trill region. For point labels, you specify the point locations and the label values. In this example, the point locations are in seconds.
peakLocsWhale1 = [1.553 1.626 1.7]; peakValsWhale1 = [0.211 0.254 0.211]; setLabelValue(lss,1,{'TrillRegions','TrillPeaks'}, ... peakLocsWhale1,peakValsWhale1,'LabelRowIndex',1); subplot(2,1,1) plot(peakLocsWhale1,peakValsWhale1,'v') hold off peakLocsWhale2 = [11.214 11.288 11.437]; peakValsWhale2 = [0.119 0.14 0.15]; setLabelValue(lss,2,{'TrillRegions','TrillPeaks'}, ... peakLocsWhale2,peakValsWhale2,'LabelRowIndex',1); subplot(2,1,2) plot(peakLocsWhale2,peakValsWhale2,'v') hold off
Explore Label Values
Explore the label values using getLabelValues
.
getLabelValues(lss)
ans=2×3 table
WhaleType MoanRegions TrillRegions
_________ ___________ ____________
Whale1 blue {3x2 table} {1x3 table}
Whale2 blue {3x2 table} {1x3 table}
Retrieve the moan regions for the first member of the labeled set.
getLabelValues(lss,1,'MoanRegions')
ans=3×2 table
ROILimits Value
____________ _____
6.1 7.7 {[1]}
11.4 13.1 {[1]}
16.5 18.1 {[1]}
Use a second output argument to list the sublabels of a label.
[value,valueWithSublabel] = getLabelValues(lss,1,'TrillRegions')
value=1×2 table
ROILimits Value
__________ _____
1.4 3.1 {[1]}
valueWithSublabel=1×3 table
ROILimits Value Sublabels
__________ _____ ___________
TrillPeaks
___________
1.4 3.1 {[1]} {3x2 table}
To retrieve the values in a sublabel, express the label name as a two-element array.
getLabelValues(lss,1,{'TrillRegions','TrillPeaks'})
ans=3×2 table
Location Value
________ __________
1.553 {[0.2110]}
1.626 {[0.2540]}
1.7 {[0.2110]}
Find the value of the third trill peak corresponding to the second member of the set.
getLabelValues(lss,2,{'TrillRegions','TrillPeaks'}, ... 'LabelRowIndex',1,'SublabelRowIndex',3)
ans=1×2 table
Location Value
________ __________
11.437 {[0.1500]}
Count Label Values and Create Datastores
Specify the path to a set of audio signals included as MAT-files with MATLAB®. Each file contains a signal variable and a sample rate. List the names of the files.
folder = fullfile(matlabroot,"toolbox","matlab","audiovideo"); lst = dir(append(folder,"/*.mat")); nms = {lst(:).name}'
nms = 7x1 cell
{'chirp.mat' }
{'gong.mat' }
{'handel.mat' }
{'laughter.mat'}
{'mtlb.mat' }
{'splat.mat' }
{'train.mat' }
Create a signal datastore that points to the specified folder. Set the sample rate variable name to Fs
, which is common to all files. Generate a subset of the datastore that excludes the file mtlb.mat
. Use the subset datastore as the source for a labeledSignalSet
object.
sds = signalDatastore(folder,"SampleRateVariableName","Fs"); sds = subset(sds,~strcmp(nms,"mtlb.mat")); lss = labeledSignalSet(sds);
Create three label definitions to label the signals:
Define a logical attribute label that is true for signals that contain human voices.
Define a numeric point label that marks the location and amplitude of the maximum of each signal.
Define a categorical region-of-interest (ROI) label to pick out nonoverlapping, uniform-length random regions of each signal.
Add the signal label definitions to the labeled signal set.
vc = signalLabelDefinition("Voice",'LabelType','attribute', ... 'LabelDataType','logical','DefaultValue',false); mx = signalLabelDefinition("Maximum",'LabelType','point', ... 'LabelDataType','numeric'); rs = signalLabelDefinition("RanROI",'LabelType','ROI', ... 'LabelDataType','categorical','Categories',["ROI" "other"]); addLabelDefinitions(lss,[vc mx rs])
Label the signals:
Label
'handel.mat'
and'laughter.mat'
as having human voices.Use the
islocalmax
function to find the maximum of each signal. Label its location and value.Use the
randROI
function to generate as many regions of length N/10 samples as can fit in a signal of length N given a minimum separation of N/6 samples between regions. Label their locations and assign them to theROI
category.
When labeling points and regions, convert sample values to time values. Subtract 1 to account for MATLAB® array indexing and divide by the sample rate.
kj = 1; while hasdata(sds) [sig,info] = read(sds); fs = info.SampleRate; [~,fn] = fileparts(info.FileName); if fn=="handel" || fn=="laughter" setLabelValue(lss,kj,"Voice",true) end xm = find(islocalmax(sig,'MaxNumExtrema',1)); setLabelValue(lss,kj,"Maximum",(xm-1)/fs,sig(xm)) N = length(sig); rois = randROI(N,round(N/10),round(N/6)); setLabelValue(lss,kj,"RanROI",(rois-1)/fs,repelem("ROI",size(rois,1))) kj = kj+1; end
Verify that only two signals contain voices.
countLabelValues(lss,"Voice")
ans=2×3 table
Voice Count Percent
_____ _____ _______
false 4 66.667
true 2 33.333
Verify that two signals have a maximum amplitude of 1.
countLabelValues(lss,"Maximum")
ans=5×4 table
Maximum Count Percent MemberCount
______________________ _____ _______ ___________
0.80000000000000004441 1 16.667 1
0.89113331915798421612 1 16.667 1
0.94730769230769229505 1 16.667 1
1 2 33.333 2
1.0575668990330560071 1 16.667 1
Verify that each signal has four nonoverlapping random regions of interest.
countLabelValues(lss,"RanROI")
ans=2×4 table
RanROI Count Percent MemberCount
______ _____ _______ ___________
ROI 24 100 6
other 0 0 0
Create two datastores with the data in the labeled signal set:
The
signalDatastore
objectsd
contains the signal data.The
arrayDatastore
objectld
contains the labeling information. Specify that you want to include the information corresponding to all the labels you created.
[sd,ld] = createDatastores(lss,["Voice" "RanROI" "Maximum"]);
Use the information in the datastores to plot the signals and display their labels.
Use a
signalMask
object to highlight the regions of interest in blue.Plot yellow lines to mark the locations of the maxima.
Add a red axis label to the signals that contain human voices.
tiledlayout flow while hasdata(sd) [sg,nf] = read(sd); lbls = read(ld); nexttile msk = signalMask(lbls{:}.RanROI{:},'SampleRate',nf.SampleRate); plotsigroi(msk,sg) colorbar off xlabel('') xline(lbls{:}.Maximum{:}.Location, ... 'LineWidth',2,'Color','#EDB120') if lbls{:}.Voice{:} ylabel('VOICED','Color','#D95319') end end
function roilims = randROI(N,wid,sep) num = floor((N+sep)/(wid+sep)); hq = histcounts(randi(num+1,1,N-num*wid-(num-1)*sep),(1:num+2)-1/2); roilims = (1 + (0:num-1)*(wid+sep) + cumsum(hq(1:num)))' + [0 wid-1]; end
Version History
Introduced in R2018b
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