The adversarial denoiser performs better than the wavelet denoiser, especially for the worst SNR values. Save the data set to a local folder or download the data use the following code. Each record contains a raw noisy ECG signal and a manually filtered clean ground truth version. This example uses the Physionet ECG-ID database, which has 310 ECG records from 90 subjects. The model use noisy source signal training set to compute Loss2 and Loss4 and clean source signal training set to compute Loss3.īecause the encoder wants to fool the discriminator, the target label for Loss4 is clean even though it is always computed using noisy signal inputs. Loss2, Loss3, and Loss4 are all cross-entropy losses for predicted labels from the discriminator. Loss1 is the mean squared error (MSE) between the generated denoised signal and the clean input signal.
#MATLAB DENOISE UPDATE#
The Adadelta optimizer is used to update the model after getting the feedback. Both the discriminator and encoder provide feedback in the form of computed loss values to update themselves and the encoder.
Finally, the decoder reconstructs the denoised signal from the latent representation. The discriminator is responsible for identifying whether the latent representation comes from a clean input signal or a noisy one.
The representation encodes enough information for the decoder to reconstruct the original signal from it. The representation does not encode any noise information and be clean enough to fool the discriminator into thinking it was encoded from a clean input signal.
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