Abstract: A process is provided for training a neural network that digitally models an audio system. A sound source is utilized to electrically couple a test signal into an input of a reference audio system. The output of the reference audio system is collected into an audio interface coupled to a computer. A neural network is then trained 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. A graphical user interface then outputs a graphical representation of the trained neural network, where the graphical representation visually displays at least one virtual control for interaction by a user.

Abstract: A neural network is trained to digitally model a reference audio system. Training is carried out by repeatedly performing a set of operations. The set of operations includes predicting by the neural network, a model output based upon an input, where the output approximates an expected output of the reference audio system, and the prediction is carried out in the time domain. The set of operations also includes applying a perceptual loss function to the neural network based upon a determined psychoacoustic property, wherein the perceptual loss function is applied in the frequency domain. Moreover, the set of operations includes adjusting the neural network responsive to the output of the perceptual loss function. A neural model file is output that can be loaded to generate a virtualization of the reference audio system.

Abstract: A neural network is trained to digitally model a reference audio system. Training is carried out by repeatedly performing a set of operations. The set of operations includes predicting by the neural network, a model output based upon an input, where the output approximates an expected output of the reference audio system, and the prediction is carried out in the time domain. The set of operations also includes applying a perceptual loss function to the neural network based upon a determined psychoacoustic property, wherein the perceptual loss function is applied in the frequency domain. Moreover, the set of operations includes adjusting the neural network responsive to the output of the perceptual loss function. A neural model file is output that can be loaded to generate a virtualization of the reference audio system.