how to train dataset

Question

Kamil Kacer 2020-11-20

0
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https://ww2.mathworks.cn/matlabcentral/answers/654598-how-to-train-dataset

评论： Walter Roberson 2020-11-20

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Hi Iam trying to train a dataset on a short term basis of 1 Sample with 3 Features so I expect 3 row matrix.

The problem is i get an empty matrix.

Do you know where the problem is ? example would be helpfull

function kNN_model_add_class(modelName, className, classPath, ...
     stWin, stStep)
%
% 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);
   Features = stFeatureExtraction(curFileName, 44100, stWin, stStep)
  
%    midFeatures = featureExtractionFile(curFileName, ...
%    stWin, stStep, mtWin, mtStep, listOfStatistics);
    % long-term averaging:
    longFeatures = mean(Features,2);       
    
    F = [F longFeatures];
end

% save the model:
if ~exist(modelName, 'file') % model does not exist --> generate     
    ClassNames{1} = className;
    Features{1} = F;   
    FileNames{1} = FileNamesTemp;
    save(modelName, 'ClassNames', 'Features', ...
         'stWin', 'stStep','FileNames');
else
    load(modelName);
    ClassNames{end+1} = className;
    Features{end+1} = F;
    FileNames{end+1} = FileNamesTemp;
    save(modelName, 'ClassNames', 'Features', ...
         'stWin', 'stStep','FileNames');
end