function - DWT/CWT and NN

% function helperCreateRGBfromTF(ads,datafolder_scallogram)
%
% for i = 1:length(ads.Files)
%     clearvars sig
%     Fs = 44100;
%     sig = audioread(ads.Files{i});
%
%
%     fb = cwtfilterbank('SignalLength',length(sig),...
%     'SamplingFrequency',Fs,...
%     'VoicesPerOctave',4);
%     cfs = abs(fb.wt(sig(:)));
%     im = ind2rgb(im2uint8(rescale(cfs)),jet(128));
%     imgLoc = fullfile(datafolder_scallogram,char(ads.Labels(i)));
%     imFileName = strcat(char(ads.Labels(i)),'_',num2str(i),'.jpg');
%     imwrite(imresize(im,[224 224]),fullfile(imgLoc,imFileName));
% end
% end

function helperCreateRGBfromTF(ads,datafolder_scallogram)

for i = 1:length(ads.Files)
    clearvars sig

    level=2;
    sig = audioread(ads.Files{i});
    cfs = abs(wavedec(sig(:),level,'rbio2.8'));

    im = ind2rgb(im2uint8(rescale(cfs)),jet(128));
    imgLoc = fullfile(datafolder_scallogram,char(ads.Labels(i)));
    imFileName = strcat(char(ads.Labels(i)),'_',num2str(i),'.jpg');
    imwrite(imresize(im,[224 224]),fullfile(imgLoc,imFileName));
end
end


% findLayersToReplace(lgraph) finds the single classification layer and the
% preceding learnable (fully connected or convolutional) layer of the layer
% graph lgraph.

function [learnableLayer,classLayer] = findLayersToReplace(lgraph)

if ~isa(lgraph,'nnet.cnn.LayerGraph')
    error('Argument must be a LayerGraph object.')
end

% Get source, destination, and layer names.
src = string(lgraph.Connections.Source);
dst = string(lgraph.Connections.Destination);
layerNames = string({lgraph.Layers.Name}');

% Find the classification layer. The layer graph must have a single
% classification layer.
isClassificationLayer = arrayfun(@(l) ...
    (isa(l,'nnet.cnn.layer.ClassificationOutputLayer')|isa(l,'nnet.layer.ClassificationLayer')), ...
    lgraph.Layers);

if sum(isClassificationLayer) ~= 1
    error('Layer graph must have a single classification layer.')
end
classLayer = lgraph.Layers(isClassificationLayer);


% Traverse the layer graph in reverse starting from the classification
% layer. If the network branches, throw an error.
currentLayerIdx = find(isClassificationLayer);
while true

    if numel(currentLayerIdx) ~= 1
        error('Layer graph must have a single learnable layer preceding the classification layer.')
    end

    currentLayerType = class(lgraph.Layers(currentLayerIdx));
    isLearnableLayer = ismember(currentLayerType, ...
        ['nnet.cnn.layer.FullyConnectedLayer','nnet.cnn.layer.Convolution2DLayer']);

    if isLearnableLayer
        learnableLayer =  lgraph.Layers(currentLayerIdx);
        return
    end

    currentDstIdx = find(layerNames(currentLayerIdx) == dst);
    currentLayerIdx = find(src(currentDstIdx) == layerNames);

end
end