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countLabelValues

Count label values

    Description

    example

    cnt = countLabelValues(lss,lblname) counts the values of the label named 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 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

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    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     
    
    

    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 the ROI 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 object sd contains the signal data.

    • The arrayDatastore object ld 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

    Figure contains 6 axes objects. Axes object 1 contains 4 objects of type line, constantline. Axes object 2 contains 4 objects of type line, constantline. Axes object 3 contains 4 objects of type line, constantline. Axes object 4 contains 4 objects of type line, constantline. Axes object 5 contains 4 objects of type line, constantline. Axes object 6 contains 4 objects of type line, constantline.

    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

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    Labeled signal set, specified as a labeledSignalSet object.

    Example: labeledSignalSet({randn(100,1) randn(10,1)},signalLabelDefinition('female')) specifies a two-member set of random signals containing the attribute 'female'.

    Label name, specified as a character vector or string scalar.

    Data Types: char | string

    Output Arguments

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    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.

    Introduced in R2021a