1-d input resnet18 to denoise in matlab

To denoise a 1D signal using the ResNet18 architecture in MATLAB, you can follow these steps:

  1. Load your signal into MATLAB using the audioread or load functions, depending on the type of signal you are working with.

  2. Preprocess the data by normalizing it to values between 0 and 1. You can use the normalize function for this.

  3. Create an image datastore using the imds function in the Image Processing Toolbox. This will allow you to read your signal data into the ResNet18 network.

  4. Load the ResNet18 network using the resnet18 function in the Deep Learning Toolbox.

  5. Modify the input layer of the ResNet18 network to accept 1D signals. You can do this by replacing the 2D input layer with a 1D convolutional layer using the replaceLayer function.

  6. Train the denoising model using the trainNetwork function in the Deep Learning Toolbox.

  7. Test the performance of your trained model on new data using the classify function in the Deep Learning Toolbox.

Here is some sample MATLAB code to get you started:

main.m
% Load your signal data
signal = audioread('myaudiofile.wav');

% Preprocess the data
signal = normalize(signal);

% Create an image datastore
imds = imageDatastore(signal);

% Load the ResNet18 network
net = resnet18;

% Modify the input layer to accept 1D signals
inputSize = net.Layers(1).InputSize;
layers = [
    imageInputLayer([inputSize(1) 1 1],'Name','input')
    convolution2dLayer([inputSize(1) 1],net.Layers(2).Weights,'Name','conv1')
    batchNormalizationLayer('Name','bn_conv1')
    reluLayer('Name','relu_conv1')
    maxPooling2dLayer([inputSize(2) 1],'Stride', [inputSize(1) 1],'Name','maxpool')
    net.Layers(4:end)
    ];

% Replace the input layer
net = replaceLayer(net,'data',layers);

% Train the denoising model
options = trainingOptions('adam', ... % Algorithm for gradient descent
                          'InitialLearnRate',0.001, ... % Learning rate for gradient descent
                          'MaxEpochs',20, ... % Maximum number of epochs to train
                          'MiniBatchSize',16, ... % Size of mini-batches
                          'Verbose',false, ... % Whether to display output during training
                          'Plots','training-progress'); % Whether to plot training progress
trainedNet = trainNetwork(imds,net,options);

% Test the performance of the model on new data
testSignal = audioread('testaudiofile.wav');
testSignal = normalize(testSignal);
noisySignal = addnoise(testSignal); % Add some noise to the signal
denoisedSignal = classify(trainedNet,noisySignal);

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Note that in the code above, the addnoise function is a placeholder for a function that adds noise to your signal data. You can implement this function using the awgn function in the Signal Processing Toolbox if you want to add white Gaussian noise.

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matlab
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