a noise signal.mat as the resnet18' input and a clean signal as its output in matlab

main.m
% Load the noise and clean signals
noise_data = load('noise_signal.mat');
clean_data = load('clean_signal.mat');

% Preprocess the signals
fs = 44100; % Sampling frequency in Hz
frame_length = 30; % frame length in ms
frame_shift = 10; % frame shift in ms

% Convert the signals to frames
noise_frames = signal2frames(noise_data.signal, frame_length, frame_shift, fs);
clean_frames = signal2frames(clean_data.signal, frame_length, frame_shift, fs);

% Split the frames into training and testing sets
num_frames = size(noise_frames, 2);
train_ratio = 0.8; % 80% training, 20% testing
train_indices = randperm(num_frames, round(num_frames * train_ratio));
test_indices = setdiff(1:num_frames, train_indices);

% Use the indices to create input/output pairs
X_train = noise_frames(:, train_indices);
Y_train = clean_frames(:, train_indices);
X_test = noise_frames(:, test_indices);
Y_test = clean_frames(:, test_indices);
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