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 robotic system is provided, which automatically changes settings on an audio system. The audio system (e.g., an instrument amplifier, effect processor, etc.) typically includes one or more controls that impact the operation of the audio system. Correspondingly, the robotic system includes a device interface coupled to a control sequencer. The device interface adapts to one or more controls of the audio system that are to be changed. In this regard, the device interface includes one or more control couplers. Each control coupler is adapted to a corresponding control of the audio system to be changed. The control sequencer provides a control sequence to the device interface that causes the control coupler(s) to vary the settings on the audio system. In practical applications, a combination of sequence values of the control sequence can represent a sufficiently high number of samples to determine a responsive behavior of the audio system.

Abstract: A system for emulating a physical audio system comprises a user interface (UI) and a digital model of the physical audio system. The UI comprises virtual controls for changing virtual control settings (e.g., a virtual volume control for changing a virtual volume setting, etc.). A change in a virtual control setting produces a change to the output of the digital model. Because the digital model emulates the behavior of the physical audio system, changes to the model output in response to changes in the virtual control settings correspond to changes in the audio output in response to changes in the physical control settings. For example, if the physical audio system is an audio amplifier with control knobs, then the virtual controls will affect the output of the digital model like the control knobs affect the audio output of the audio amplifier.

Abstract: A gyral-linear actuator for an encoder comprises an actuator housing having a first end, a second end opposite the first end, and a hollow. A control member extends from the first end of the actuator housing. A spring is positioned within the hollow of the actuator housing. Moreover, a coupler has an encoder connector and an extension that couples the control member to the encoder connector. Under this configuration, when the encoder connector is coupled to a rotary shaft of the encoder, rotation of the control member causes corresponding rotation of the encoder connector so as to turn the rotary shaft of the encoder, and depression of the control member causes corresponding depression of the rotary shaft of the encoder operating a switch function of the rotary encoder.

Abstract: A gyral-linear actuator for an encoder comprises an actuator housing having a first end, a second end opposite the first end, and a hollow. A control member extends from the first end of the actuator housing. A spring is positioned within the hollow of the actuator housing. Moreover, a coupler has an encoder connector and an extension that couples the control member to the encoder connector. Under this configuration, when the encoder connector is coupled to a rotary shaft of the encoder, rotation of the control member causes corresponding rotation of the encoder connector so as to turn the rotary shaft of the encoder, and depression of the control member causes corresponding depression of the rotary shaft of the encoder operating a switch function of the rotary encoder.

Abstract: A gyral-linear actuator for an encoder comprises an actuator housing having a first end, a second end opposite the first end, and a hollow. A control member extends from the first end of the actuator housing. A spring is positioned within the hollow of the actuator housing. Moreover, a coupler has an encoder connector and an extension that couples the control member to the encoder connector. Under this configuration, when the encoder connector is coupled to a rotary shaft of the encoder, rotation of the control member causes corresponding rotation of the encoder connector so as to turn the rotary shaft of the encoder, and depression of the control member causes corresponding depression of the rotary shaft of the encoder operating a switch function of the rotary encoder.

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.