how to train dataset

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Kamil Kacer
Kamil Kacer 2020년 11월 20일
댓글: Walter Roberson 2020년 11월 20일
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
  댓글 수: 1
Walter Roberson
Walter Roberson 2020년 11월 20일
Are you saying that longFeatures is empty after stFeatureExtraction ?
Or is it possible that nothing is being matched in D and so stFeatureExtraction is not being called?

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