wave_process.m

clc;
clf('reset');
close all hidden;
%Wave segment will generate new data which has been segmented to aling S1
%and S2 peaks in the audio data
%wave_segment;
%% Load the data from the given directory
filePattern = fullfile('t2','set_a','*.wav');
dirListings = dir(filePattern);
dir_len = length(dirListings);
fileID = fopen('set_a_timing.csv');
data = textscan(fileID,'%s %s %s %s','Delimiter',',');
categories ={'Artifact','Extrahls','Murmur','Normal','Test_Artifact','Test_Extrahls','Test_Murmur','Test_Normal','Unlabelled'};
len = 3099;
%% Here you can modify the number of test files for training
test_artifact = 26;
test_extrahls = 26;
test_murmur = 26;
test_normal = 26;
%% Generate the relevant directory for saving the outputs the files
search_dir = fullfile('t2','images');
rmdir(search_dir,'s');
mkdir(search_dir);
for k = 1:numel(categories)
   if(exist(search_dir,'dir') == 7)
        mkdir(fullfile('t2','images',char(categories(k))));
   end
end
%% The the wave files are read, normalized and filtered and categorized images are produced
disp('Generating Unlabelled data...');
root_input = fullfile('t2','set_a');
root_output = fullfile('t2','images');
%For the unlabelled data
for d = 1:52
    [y,fs] = audioread(fullfile(root_input,dirListings(d).name));
    
    
    %Settings to set position of figure axes 
    axes('Units', 'normalized', 'Position', [0 0 1 1])
    F =linspace(1,600,1000);
    
    %Generate the sectrogram and save gcf to fig
    spectrogram(y,500,[],F,fs,'yaxis');
    colormap gray;
    fig = gcf;
    
    %Configurations to turn off various features of the figure
    set(fig,'Visible','off');
    colorbar off;
    axis off;
    iptsetpref('ImshowBorder','tight');
    
    %Save the image to file 
    frame = getframe(fig);
    I = frame.cdata;
    I = imresize(I,[525 700]);
    imwrite(I,strcat(fullfile(root_output,'Unlabelled',dirListings(d).name(1:end-4)),'.png'),'png');
    close all hidden;
    clf;
end


% %% 
% disp('Generating Artifact data...');
% %For the artifact data
% for d = 53:92
%     [y,fs] = audioread(fullfile(root_input,dirListings(d).name));  
%     
%     %The data is normalized using generalized min-max method.
%     norm_y = ((y-min(y))/(max(y) - min(y)))*(1+1)-1;
%     
%     %Settings to set position of figure axes
%     axes('Units', 'normalized', 'Position', [0 0 1 1])
%     F =linspace(1,600,1000);
%     
%     %Generate the sectrogram and save gcf to fig
%     spectrogram(y,500,[],F,fs,'yaxis');
%     colormap gray;
%     fig = gcf;
%     
%     %Configurations to turn off various features of the figure
%     set(fig,'Visible','off');
%     colorbar off;
%     axis off;
%     iptsetpref('ImshowBorder','tight');
%     
%     %Save the image to file
%     frame = getframe(fig);
%     I = frame.cdata;
%     I = imresize(I,[525 700]);
%     
%     if(d >= 53+test_artifact)
%         imwrite(I,strcat(fullfile(root_output,'Test_Artifact',dirListings(d).name(1:end-4)),'.png'),'png');
%         close all hidden;
%         clf;
%     else
%         imwrite(I,strcat(fullfile(root_output,'Artifact',dirListings(d).name(1:end-4)),'.png'),'png');
%         close all hidden;
%         clf;
%     end
% end
% disp('Generating Extrahls data...');
% %For the extrahls data
% for d = 93:119
%     [y,fs] = audioread(fullfile(root_input,dirListings(d).name));  
%     
%     %The data is normalized using generalized min-max method.
%     norm_y = ((y-min(y))/(max(y) - min(y)))*(1+1)-1;
%     
%     %Settings to set position of figure axes
%     axes('Units', 'normalized', 'Position', [0 0 1 1])
%     F =linspace(1,600,1000);
%     
%     %Generate the sectrogram and save gcf to fig
%     spectrogram(y,500,[],F,fs,'yaxis');
%     colormap gray;
%     fig = gcf;
%     
%     %Configurations to turn off various features of the figure
%     set(fig,'Visible','off');
%     colorbar off;
%     axis off;
%     iptsetpref('ImshowBorder','tight');
%     
%     %Save the image to file
%     frame = getframe(fig);
%     I = frame.cdata;
%     I = imresize(I,[525 700]);
%     if(d >= 93+test_extrahls)
%         imwrite(I,strcat(fullfile(root_output,'Test_Extrahls',dirListings(d).name(1:end-4)),'.png'),'png');
%         close all hidden;
%         clf;
%     else
%         imwrite(I,strcat(fullfile(root_output,'Extrahls',dirListings(d).name(1:end-4)),'.png'),'png');
%         close all hidden;
%         clf;
%     end
% end
% disp('Generating Murmur data...');
% %For the Murmur data
% for d = 120:153
%     [y,fs] = audioread(fullfile(root_input,dirListings(d).name));  
%     
%     %The data is normalized using generalized min-max method.
%     norm_y = ((y-min(y))/(max(y) - min(y)))*(1+1)-1;
%     
%     %Settings to set position of figure axes
%     axes('Units', 'normalized', 'Position', [0 0 1 1])
%     F =linspace(1,600,1000);
%     
%     %Generate the sectrogram and save gcf to fig
%     spectrogram(y,500,[],F,fs,'yaxis');
%     colormap gray;
%     fig = gcf;
%     
%     %Configurations to turn off various features of the figure
%     set(fig,'Visible','off');
%     colorbar off;
%     axis off;
%     iptsetpref('ImshowBorder','tight');
%    
%     %Save the image to the file
%     frame = getframe(fig);
%     I = frame.cdata;
%     I = imresize(I,[525 700]);
%     if(d >= 120+test_murmur)
%         imwrite(I,strcat(fullfile(root_output,'Test_Murmur',dirListings(d).name(1:end-4)),'.png'),'png');
%         close all hidden;
%         clf;
%     else
%         imwrite(I,strcat(fullfile(root_output,'Murmur',dirListings(d).name(1:end-4)),'.png'),'png');
%         close all hidden;
%         clf;
%     end
% end
% disp('Generating Normal data...');
% %For the Normal data
% for d = 154:184
%     [y,fs] = audioread(fullfile(root_input,dirListings(d).name));  
%     
%     %The data is normalized using generalized min-max method.
%     norm_y = ((y-min(y))/(max(y) - min(y)))*(1+1)-1;
%     
%     %Settings to set position of figure axes
%     axes('Units', 'normalized', 'Position', [0 0 1 1])
%     F =linspace(1,600,1000);
%     
%     %Generate the sectrogram and save gcf to fig
%     spectrogram(y,500,[],F,fs,'yaxis');
%     colormap gray;
%     fig = gcf;
%     
%     %Configurations to turn off various features of the figure
%     set(fig,'Visible','off');
%     colorbar off;
%     axis off;
%     iptsetpref('ImshowBorder','tight');
%     
%     %Save the image to the file
%     frame = getframe(fig);
%     I = frame.cdata;
%     I = imresize(I,[525 700]);
%     if(d >= 154+test_normal)
%         imwrite(I,strcat(fullfile(root_output,'Test_Normal',dirListings(d).name(1:end-4)),'.png'),'png');
%         close all hidden;
%         clf;
%     else
%         imwrite(I,strcat(fullfile(root_output,'Normal',dirListings(d).name(1:end-4)),'.png'),'png');
%         close all hidden;
%         clf;
%     end
% end
% %% Loading and processing a bit quicker.
% rootFolder = fullfile('t2','images');
% 
% %Train with all data except the unlabelled stuff
% trainData = imageDatastore(fullfile(rootFolder, categories(1:4)), 'LabelSource', 'foldernames');
% testData = imageDatastore(fullfile(rootFolder, categories(5:8)), 'LabelSource', 'foldernames');
% %Show a summary of each labelr
% tb1 = countEachLabel(trainData);
% 
% %Determine the minimum no of images
% minSetCount = min(tb1{:,2});
% 
% %Split the data into testData and trainData
% %[trainData,testData] = splitEachLabel(trainData,15);
% 
% 
% %Count the number of test data
% tb2 = countEachLabel(testData)
% 
% %Modifying the labels for the trainData
% total = sum(tb2{1:4,2});
% for i = 1:total
%     if(testData.Labels(i) == "Test_Artifact")
%         testData.Labels(i) = "Artifact";
%     elseif(testData.Labels(i) == "Test_Extrahls")
%         testData.Labels(i) = "Extrahls";
%     elseif(testData.Labels(i) == "Test_Murmur")
%         testData.Labels(i) = "Murmur";
%     elseif(testData.Labels(i) == "Test_Normal")
%         testData.Labels(i) = "Normal";
%     end
%         
% end
% 
% 
% %Count the number of training data
% countEachLabel(trainData)