Signal denoising based on adaptable deep neural networks

A method, comprising:

• in an electronic device:
• receiving training data comprising a first bio-signal and a second bio-signal that is different from the first bio-signal;
• computing a weighted sum of the first bio-signal and the second bio-signal;
• generating a mixed signal based on the weighted sum;
• generating an output signal based on application of a denoising neural network (DNN) on the mixed signal;
• computing a loss based on a comparison of the output signal with the first bio-signal;
• and
• training the DNN for a number of epochs until the computed loss is below a threshold.

A non-transitory computer-readable medium having stored thereon, computer-executable instructions that when executed by an electronic device, causes the electronic device to execute operations, the operations comprising:

• receiving training data comprising a first bio-signal and a second bio-signal that is different from the first bio-signal;
• computing a weighted sum of the first bio-signal and the second bio-signal;
• generating a mixed signal based on the weighted sum;
• generating an output signal based on application of a denoising neural network (DNN) on the mixed signal;
• computing a loss based on a comparison of the output signal with the first bio-signal;
• and
• training the DNN for a number of epochs until the computed loss is below a threshold.