To convert audio signals to Discrete Wavelet Transform (DWT) images and save them according to their labels, you can modify the function you provided for CWT. Below is a concise guide and a modified function tailored for DWT.
Modified Function for DWT
This function, helperCreateImagesFromDWT, processes audio files, applies DWT, and saves the coefficients as images in labeled folders.
function helperCreateImagesFromDWT(ads, outputFolder)
for i = 1:length(ads.Files)
[audioIn, info] = audioread(ads.Files{i}); % Read audio file
[cA, cD] = dwt(audioIn, 'db1'); % Apply DWT with 'db1' wavelet
% Process and normalize coefficients
coeffs = [cA; cD];
coeffsNorm = (coeffs - min(coeffs)) / (max(coeffs) - min(coeffs));
img = im2uint8(mat2gray(coeffsNorm)); % Convert to image
% Prepare image file path
label = char(ads.Labels(i));
imgFileName = strcat(label, '_', num2str(i), '.png');
imgFolderPath = fullfile(outputFolder, label);
if ~exist(imgFolderPath, 'dir'), mkdir(imgFolderPath); end % Ensure folder exists
imwrite(imresize(img, [224, 224]), fullfile(imgFolderPath, imgFileName)); % Save image
end
end
Applying the Function
- Set Up the Output Folder: Change datafolder_scallogram to a new folder name that reflects you're working with DWT images, for example, datafolder_dwtImages.
- Call the Function: Replace the call to helperCreateRGBfromTF in your script with helperCreateImagesFromDWT, passing the audio datastore and the new output folder path.
datafolder_dwtImages = "C:\Users\leere\Documents\MATLAB\DWT_Images"; % New folder for DWT images
helperCreateImagesFromDWT(ads, datafolder_dwtImages); % Process and save DWT images
This approach applies DWT to your audio signals, normalizes and processes the coefficients to create images, and organizes these images in folders based on their labels, ready for neural network classification.
