Separating noise to increase machine learning prediction accuracy in a test and measurement system

A test and measurement instrument, comprising:

• an input port to allow the instrument to receive one or more waveforms from a device under test (DUT);
• two or more deep learning networks;
• one or more low pass filters to remove a portion of the noise from the one or more waveforms to produce one or more filtered waveforms to increase accuracy of predictions from the two or more deep learning networks;
• and
• one or more processors configured to execute code that causes the one or more processors to:
• select one or more waveform patterns from the one or more waveforms;
• measure noise in the one or more waveforms and generate a noise representation of the noise removed from the one or more waveforms;
• create one or more images for the one or more waveforms using the one or more waveform patterns and the one or more filtered waveforms;
• add the noise representation to the one or more images for the one or more waveforms to produce at least one combined image;
• input the at least one combined image to the two or more deep learning networks comprised of at least one first deep learning network trained to produce predicted filter tap values, and at least one second deep learning network trained to produce one of either predicted performance measurement values or predicted operating parameters for the DUT;
• and
• receive one or more of the predicted filter tap values, and the one of either predicted performance measurement values or predicted operating parameters for the DUT from the two or more deep learning networks.