System and methods for generating realistic waveforms

A system for training a generative adversarial network including a Generator and a Discriminator by performing one or more training cycles, comprising:

• one or more electronic processors operable to execute a set of computer-executable instructions;
• and
• the set of computer-executable instructions stored in a non-transitory medium, wherein when executed, the instructions cause the one or more electronic processors to
• input a first tensor including a set of elements to the Generator, each tensor element being a value and the set of elements representing a distribution of values;
• operate the Generator to output a tensor representing a generated distribution of values;
• process the output tensor to convert the generated distribution of values to the frequency domain;
• determine one or more error terms from the converted generated distribution;
• obtain a second tensor including a set of elements, each tensor element being a value and the set of elements representing an actual distribution of values;
• convert the actual distribution of values to the frequency domain;
• determine one or more error terms from the converted actual distribution;
• input the converted generated distribution and the converted actual distribution to the Discriminator;
• operate the Discriminator to determine an adversarial loss term, the adversarial loss term including a Generator loss term and a Discriminator loss term;
• and
• use the Generator loss term, the error term from the converted generated distribution and the error term from the converted actual distribution to modify the Generator and use the Discriminator loss term to modify the Discriminator.

A non-transitory medium including a set of computer-executable instructions that when executed by one or more programmed electronic processors, cause the processors to train a generative adversarial network including a Generator and a Discriminator by performing one or more training cycles by:

• inputting a first tensor including a set of elements to the Generator, each tensor element being a value and the set of elements representing a distribution of values;
• operating the Generator to output a tensor representing a generated distribution of values;
• processing the output tensor to convert the generated distribution of values to the frequency domain;
• determining one or more error terms from the converted generated distribution;
• obtaining a second tensor including a set of elements, each tensor element being a value and the set of elements representing an actual distribution of values;
• converting the actual distribution of values to the frequency domain;
• determining one or more error terms from the converted actual distribution;
• inputting the converted generated distribution and the converted actual distribution to the Discriminator;
• operating the Discriminator to determine an adversarial loss term, the adversarial loss term including a Generator loss term and a Discriminator loss term;
• and
• using the Generator loss term, the error term from the converted generated distribution and the error term from the converted actual distribution to modify the Generator and using the Discriminator loss term to modify the Discriminator.