Neural modeler of audio systems

A process for training a neural network that digitally models an audio system, comprising:

• utilizing a sound source to electrically couple a test signal into an input of a reference audio system;
• collecting an output of the reference audio system into an audio interface coupled to a computer to store captured information;
• training a neural network using the test signal and the captured information to derive a set of weight vectors with appropriate values such that the overall output of the neural network converges towards an output representative of the reference audio system, and a signal in the time domain from a musical instrument is processed through the trained neural network with a latency under 20 milliseconds;
• outputting to a graphical user interface, a graphical representation of the trained neural network, the graphical representation visually displaying at least one virtual control;
• and
• enabling a user to interact with the virtual control of the graphical representation of the trained neural network via the graphical user interface to define a virtualization of the reference audio system;
• wherein training the neural network further comprises modeling a non-linear behavior of the reference audio system, modeling a first linear aspect of the reference audio system, and modeling a second linear aspect of the reference audio system.

A process for creating digital audio systems, comprising:

• utilizing a sound source to electrically couple a test signal into an input of a reference audio system;
• collecting an output of the reference audio system into an audio interface coupled to a computer to store captured information;
• training a neural network using the test signal and the captured information to derive a set of weight vectors with appropriate values such that the overall output of the neural network converges towards an output representative of the reference audio system, wherein the training digitally models a non-linear behavior of the reference audio system, models a first linear aspect of the reference audio system, and models a second linear aspect of the reference audio system, the training carried out by repeatedly performing operations comprising:
• predicting by the neural network, a model output based upon an input, where the output approximates an expected output of the reference audio system;
• computing an error in the prediction;
• and
• adjusting the weight vectors to minimize the computed error;
• outputting a neural network model file upon training;
• outputting to a graphical user interface, a graphical representation of the trained neural network in the neural network model file, the graphical representation visually displaying at least one virtual control;
• and
• enabling a user to interact with the virtual control of the graphical representation of the trained neural network via the graphical user interface to define a virtualization of the reference audio system.