countLabelValues
Description
counts the values of the label named cnt
= countLabelValues(lss
,lblname
)lblname
and returns results in
table cnt
. cnt
contains label value counts and
percentages. When lblname
is an ROI or point label,
cnt
also contains the number of members with at least one value of a
particular category. countLabelValues
does not support:
Sublabels
Label definitions with the LabelDataType (Signal Processing Toolbox) property set to
'table'
or'timetable'
Labels with instance values that cannot be converted to a vector with a discrete set of categories. It must be possible to group label values using a set of unique discrete categories. Examples of labels that are not supported include:
Cell arrays of timetables
Cell arrays containing matrices of different sizes
Examples
Count Label Values
Load a labeled signal set containing recordings of whale songs.
load whales
lss
lss = labeledSignalSet with properties: Source: {2x1 cell} NumMembers: 2 TimeInformation: "sampleRate" SampleRate: 4000 Labels: [2x3 table] Description: "Characterize wave song regions" Use labelDefinitionsHierarchy to see a list of labels and sublabels. Use setLabelValue to add data to the set.
Get the names of the labels in the set.
getLabelNames(lss)
ans = 3x1 string
"WhaleType"
"MoanRegions"
"TrillRegions"
Verify that the two members of the set are blue whales.
countLabelValues(lss,"WhaleType")
ans=3×3 table
WhaleType Count Percent
_________ _____ _______
blue 2 100
humpback 0 0
white 0 0
Verify that each member has three moan regions.
countLabelValues(lss,"MoanRegions")
ans=2×4 table
MoanRegions Count Percent MemberCount
___________ _____ _______ ___________
false 0 0 0
true 6 100 2
Verify that each member has one trill region.
countLabelValues(lss,"TrillRegions")
ans=2×4 table
TrillRegions Count Percent MemberCount
____________ _____ _______ ___________
false 0 0 0
true 2 100 2
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
(Signal Processing Toolbox) 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
(Signal Processing Toolbox) 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
Input Arguments
lss
— Labeled signal set
labeledSignalSet
object
Labeled signal set, specified as a labeledSignalSet
object.
Example:
specifies a two-member set of random
signals containing the attribute labeledSignalSet
({randn(100,1)
randn(10,1)},signalLabelDefinition('female'))'female'
.
lblname
— Label name
character vector | string scalar
Label name, specified as a character vector or string scalar.
Data Types: char
| string
Output Arguments
cnt
— Results table
table
Results table, returned as a table with the following variables:
Count
— Number of label values for a particular category.Percent
— Number of label values for a particular category as a percentage of all label values.MemberCount
— Number of members with at least one value of a particular category. This variable is returned only for an ROI or a point label.
Version History
Introduced in R2021a
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