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button_action.m
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visualization_map=colormap(jet(512));
visualization_map(1,:)=1;
PATH_o=cd;
if menu_style==0
axes(axe1);
datetick('x','keeplimits');
caxis([floor(min(data(:)))-ceil(max(data(:)))/20 ceil(max(data(:)))]); colormap(visualization_map); colorbar;
elseif floor(menu_style)==1
% Load data
if menu_style==1.1
[File,LTSA_folder] = uigetfile('*.mat');
[output.LTS, f, time_vec]=LTSA_combine(LTSA_folder, 0, 1, File);
% Floor to the second digits
output.LTS=floor(output.LTS*100)/100;
data=output.LTS(:,:,1);
axes(axe1); cla(axe1,'reset');
LTS_disp=3; clear temp
elseif menu_style==1.2
[LTSA_folder] = uigetdir;
[output.LTS, f, time_vec]=LTSA_combine(LTSA_folder, 0, 1);
% Floor to the second digits
output.LTS=floor(output.LTS*100)/100;
data=output.LTS(:,:,1);
axes(axe1); cla(axe1,'reset');
LTS_disp=3;
end
if menu_style>1.2
axes(axe1); lts_boundary=[axe1.XLim; axe1.YLim];
end
title_text=[{'Median PSD'}, {'Mean PSD'}, {'Difference PSD'}];
if menu_style>=1.1
% Change view between median and mean PSD
LTS_disp=rem(LTS_disp,3)+1; data=output.LTS(:,:,LTS_disp);
end
axes(axe1); cla(axe1,'reset');
imagesc(time_vec, f/1000, data); axis xy; title(title_text{LTS_disp}); save_title=title_text{LTS_disp};
xlabel('Date'); ylabel('Frequency (kHz)'); caxis([floor(min(data(:)))-ceil(max(data(:)))/20 ceil(max(data(:)))]); colormap(visualization_map); colorbar;
if menu_style>1.2
xlim(lts_boundary(1,:)); ylim(lts_boundary(2,:));
end
datetick('x','keeplimits');
elseif floor(menu_style)==2
if exist('output')==0
PATHNAME_o=cd;
[~,LTSA_folder] = uigetfile('*.mat');
[output.LTS, f, time_vec]=LTSA_combine(LTSA_folder, 0, 1);
% Floor to the second digits
output.LTS=floor(output.LTS*100)/100;
data=output.LTS(:,:,1);
end
if menu_style==2.2
% Unsupervised separation by UNMF
cd(PATH_o)
PSD_threshold=str2num(get(h20_min_PSD, 'String'));
k=str2num(get(h20_number, 'String'));
time_frame=str2num(get(h20_iter, 'String'));
sparseness_W=str2num(get(h20_sW, 'String'));
close(h20);
data_list=isnan(sum(data,1))~=1;
data=data-PSD_threshold; data(data<0)=0;
[data, W, W_cluster, H]=LTSA_PCNMF(data(:,data_list), k, time_frame, 'seminmf', sparseness_W);
output.separation=repmat(output.LTS(:,:,1),1,1,k);
for n=1:k
output.separation(:,data_list,n)=data(:,:,n);
end
cd(PATH_o);
title_separation=[{'Separated data'} {'Original data'}];
UNMF_disp=1; comp_disp=2;
elseif menu_style==2.3
% Load previous model
[model_file,model_folder] = uigetfile('*.mat');
cd(model_folder); load(model_file); cd(PATH_o);
% Supervised separation by UNMF
cd(PATH_o)
data_list=isnan(sum(data,1))~=1;
W=save_pcnmf.W;
W_cluster=save_pcnmf.W_cluster;
k=save_pcnmf.k;
time_frame=save_pcnmf.time_frame;
PSD_threshold=save_pcnmf.PSD_threshold;
sparseness_W=save_pcnmf.sparseness_W;
data=data-PSD_threshold; data(data<0)=0;
[data, W, W_cluster, H]=LTSA_PCNMF(data(:,data_list), k, time_frame, 'seminmf', sparseness_W, size(W,2), 200, W, [], W_cluster);
output.separation=repmat(output.LTS(:,:,1),1,1,k);
for n=1:k
output.separation(:,data_list,n)=data(:,:,n);
end
cd(PATH_o);
title_separation=[{'Separated data'} {'Original data'}];
UNMF_disp=1; comp_disp=2;
end
% Plot data
if menu_style==2.1
UNMF_disp=rem(UNMF_disp,k)+1;
comp_disp=1;
else
comp_disp=rem(comp_disp,2)+1;
end
if comp_disp==2
data=output.LTS(:,:,LTS_disp);
elseif comp_disp==1
data=output.separation(:,:,UNMF_disp);
end
fig_boundary=axis(axe1);
axes(axe1); cla(axe1,'reset');
imagesc(time_vec, f/1000, data); axis xy; title(title_separation{comp_disp}); save_title2=[title_separation{comp_disp} '_Component_' num2str(comp_disp)];
xlim(fig_boundary(1:2));
xlabel('Date'); ylabel('Frequency (kHz)'); datetick('x','keepticks','keeplimits');
caxis([floor(min(data(:)))-ceil(max(data(:)))/20 ceil(max(data(:)))]); colormap(visualization_map); colorbar;
elseif floor(menu_style)==3
if menu_style==3.1
[Cluster_file,clustering_folder] = uigetfile('*.mat');
cd(clustering_folder); load(Cluster_file); cd(PATH_o);
save_result=save_clustering.save_result;
time_vec=save_clustering.time_vec;
soundscape_scene=save_clustering.soundscape_scene;
f=save_clustering.f;
clustering_interface;
elseif menu_style==3.2
if exist('data')==0
msgbox('Please load long-term spectrogram at first.')
else
% Filtering input data
classification_th=str2num(get(h20_th, 'String'));
var_th=1-str2num(get(h20_var, 'String'))/100;
close(h20);
analysis_data=data;
analysis_data(analysis_data<classification_th)=classification_th; analysis_data=analysis_data-classification_th;
Unsupervised_classify;
clustering_interface;
end
end
elseif floor(menu_style)==4
if menu_style==4.1
save_output.time_vec=time_vec;
save_output.f=f;
save_output.data=data;
save_output.input=save_title;
save_output.display=save_title2;
uisave('save_output');
elseif menu_style==4.2
save_pcnmf.W=W;
save_pcnmf.W_cluster=W_cluster;
save_pcnmf.k=k;
save_pcnmf.time_frame=time_frame;
save_pcnmf.PSD_threshold=PSD_threshold;
save_pcnmf.sparseness_W=sparseness_W;
uisave('save_pcnmf');
elseif menu_style==4.3
save_clustering.save_result=save_result;
save_clustering.time_vec=time_vec;
save_clustering.soundscape_scene=soundscape_scene;
save_clustering.classification_th=classification_th;
save_clustering.f=f;
uisave('save_clustering');
Recording_time=save_result(:,1)-693960;
Cluster=save_result(:,2);
T = table(Recording_time, Clustering);
writetable(T,'soundscape_clustering.csv','Delimiter',',');
end
end