How do I create an open class classifier

Question

Kamil Kacer 2020-11-18

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/652273-how-do-i-create-an-open-class-classifier

评论： Walter Roberson 2020-11-18

I am calling function kNN_model_add_class to trained and create one class call dogs.

How do I create an 'Open class' where there will be all the other sounds(i do not want to specify them just all the other)

I am trying to make One vs all classifier.. Dog or no Dog

Thanks

strDir = 'C:\Users\User\Desktop\3rd year\Nahravky\cats_dogs';
Statistics = {'mean', 'median', 'std', 'stdbymean', 'max', 'min'};
stWin = 0.040;
stStep = 0.040;
mtWin = 2;
mtStep = 1;
kNN_model_add_class('Mfcctrain.mat', 'dogs', [ strDir '\trainpes'], ...
    Statistics, stWin, stStep, mtWin, mtStep);

function kNN_model_add_class

function kNN_model_add_class(modelName, className, classPath, ...
    listOfStatistics, stWin, stStep, mtWin, mtStep)
%
% function kNN_model_add_class(modelName, className, classPath, ...
%         listOfStatistics, stWin, stStep, mtWin, mtStep)
%
% This function adds an audio class to the kNN classification model
% 
% ARGUMENTS;
% - modelName:  the filename of the model (mat file)
% - className:  the name of the audio class to be added to the model
% - classPath:  the path of the directory where the audio segments of the
%               new class are stored
% - listOfStatistics:   list of mid-term statistics (cell array)
% - stWin, stStep:      short-term window size and step
% - mtWin, mtStep:      mid-term window size and step
%
% Example:
% kNN_model_add_class('modelSpeech.mat', 'speech', './Music/', ...
%  {'mean','std',}, 0.050, 0.025, 2.0, 1.0);
%
if ~exist(classPath,'dir')
    error('Audio sample path is not valid!');
else
    classPath = [classPath filesep];
end
% check if the model elaready exists:
fp = fopen(modelName, 'r');
if fp>0 % check if file already exists
    load(modelName);
end
% Feature extraction:
D = dir([classPath '*.wav']);
F = [];
for (i=1:length(D)) % for each wav file in the given path:
    curFileName = [classPath D(i).name];
    FileNamesTemp{i} = curFileName;
    % mid-term feature extraction for each wav file:
   [data, fs] = audioread(curFileName);
   signal = struct('Filt_data', data, 'SampleRate', fs);
   midFeatures = featureExtractionFile(signal, ...
   stWin, stStep, mtWin, mtStep, listOfStatistics);
%    midFeatures = featureExtractionFile(curFileName, ...
%    stWin, stStep, mtWin, mtStep, listOfStatistics);
    % long-term averaging:
    longFeatures = mean(midFeatures,2);       
    
    F = [F longFeatures];
end
% save the model:
Statistics = listOfStatistics;
fp = fopen(modelName, 'r');
if fp<0 % model does not exist --> generate     
    ClassNames{1} = className;
    Features{1} = F;   
    FileNames{1} = FileNamesTemp;
    save(modelName, 'ClassNames', 'Features', ...
        'Statistics', 'stWin', 'stStep', 'mtWin', 'mtStep', 'FileNames');
else
    load(modelName);
    ClassNames{end+1} = className;
    Features{end+1} = F;
    FileNames{end+1} = FileNamesTemp;
    save(modelName, 'ClassNames', 'Features', ...
        'Statistics', 'stWin', 'stStep', 'mtWin', 'mtStep', 'FileNames');
end

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Walter Roberson 2020-11-18

在 MATLAB Online 中打开

function kNN_model_add_class(modelName, className, classPath, ...
    listOfStatistics, stWin, stStep, mtWin, mtStep)
%
% function kNN_model_add_class(modelName, className, classPath, ...
%         listOfStatistics, stWin, stStep, mtWin, mtStep)
%
% This function adds an audio class to the kNN classification model
% 
% ARGUMENTS;
% - modelName:  the filename of the model (mat file)
% - className:  the name of the audio class to be added to the model
% - classPath:  the path of the directory where the audio segments of the
%               new class are stored
% - listOfStatistics:   list of mid-term statistics (cell array)
% - stWin, stStep:      short-term window size and step
% - mtWin, mtStep:      mid-term window size and step
%
% Example:
% kNN_model_add_class('modelSpeech.mat', 'speech', './Music/', ...
%  {'mean','std',}, 0.050, 0.025, 2.0, 1.0);
%
if ~exist(classPath,'dir')
    error('Audio sample path is not valid!');
else
    classPath = [classPath filesep];
end
% check if the model elaready exists:
fp = fopen(modelName, 'r');
if fp>0 % check if file already exists
    load(modelName);
end
% Feature extraction:
D = dir([classPath '*.wav']);
F = [];
for (i=1:length(D)) % for each wav file in the given path:
    curFileName = [classPath D(i).name];
    FileNamesTemp{i} = curFileName;
    % mid-term feature extraction for each wav file:
   [data, fs] = audioread(curFileName);
   signal = struct('Filt_data', data, 'SampleRate', fs);
   midFeatures = featureExtractionFile(signal, ...
   stWin, stStep, mtWin, mtStep, listOfStatistics);
%    midFeatures = featureExtractionFile(curFileName, ...
%    stWin, stStep, mtWin, mtStep, listOfStatistics);
    % long-term averaging:
    longFeatures = mean(midFeatures,2);       
    
    F = [F longFeatures];
end
% save the model:
Statistics = listOfStatistics;
if ~exist(modelName, 'file') % model does not exist --> generate     
    ClassNames{1} = className;
    Features{1} = F;   
    FileNames{1} = FileNamesTemp;
    save(modelName, 'ClassNames', 'Features', ...
        'Statistics', 'stWin', 'stStep', 'mtWin', 'mtStep', 'FileNames');
else
    load(modelName);
    ClassNames{end+1} = className;
    Features{end+1} = F;
    FileNames{end+1} = FileNamesTemp;
    save(modelName, 'ClassNames', 'Features', ...
        'Statistics', 'stWin', 'stStep', 'mtWin', 'mtStep', 'FileNames');
end

Kamil Kacer 2020-11-18

Thanks but how to i create an open class

Walter Roberson 2020-11-18

You do not create an open class. A specific open class would have to be trained as having particular characteristics, and anything that did not have those characteristics would not match.

Instead you define a threshold for matching each of your defined classes, and if the image does not meet the threshold for any of the defined classes then you say that it is Other.

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How do I create an open class classifier

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