Robotic system for controlling audio systems
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.
1. A system for training a model of an audio system, comprising:
a signal generator that is configured to provide an input signal to the audio system;
a receiver that is configured to receive an output signal from the audio system; and
a processor that is configured to control the signal generator and the receiver such that:
upon adjustment of a control of the audio system to a first control setting, the processor controls the signal generator to provide the input signal to the audio system, wherein the receiver receives the output signal, corresponding to a first output signal that represents a response of the audio system at the first control setting; and
upon adjustment of the control of the audio system to a second control setting, the processor controls the signal generator to provide the input signal to the audio system, and the receiver receives the output signal, corresponding to a second output signal that represents a response of the audio system at the second control setting;
wherein a neural network is trained, based upon the first control setting, the first output signal, the second control setting, and the second output signal, to emulate a responsive behavior of the audio system at least across a range of settings of the audio system corresponding to the first control setting and the second control setting.
2. The system of claim 1 , wherein:
the processor trains the neural network; and
the processor is implemented in a select one of a computer or a dedicated control device.
3. The system of claim 1 , wherein:
the processor communicates the first control setting, the first output signal, the second control setting, and the second output signal, to a remote processing device; and
the neural network is trained by the remote processing device.
4. The system of claim 1 further comprising a control sequencer that is configured to provide control sequences, each control sequence having sequence value that causes changes to a corresponding control associated with the audio system;
wherein:
a first control sequence associates a first sequence value with the first control setting;
a second control sequence associates a second sequence value with the second control setting; and
a combination of the sequence values across the control sequences represents a sufficiently high number of samples that are used to determine a responsive behavior of the audio system, at least across the range of settings corresponding to the sequence values.
5. The system of claim 4 , wherein:
the control sequences present sequence values for a given control of the audio system in a non-linear, non-stepwise manner.
6. The system of claim 4 further comprising:
a motor controller communicatively coupled to the processor, the motor controller operative, responsive to at least one instruction from the processor, to cause a motor to mechanically set the control of the audio system to at least one of the first control setting or the second control setting, based upon the control sequence from the control sequencer.
7. The system of claim 1 , wherein:
the neural network is configured to emulate the responsive behavior of the audio system at any given control setting of the control, within a control space that is defined by the first control setting and the second control setting.
8. A system for capturing an audio system, comprising:
a signal generator that is configured to provide an input signal to the audio system;
a receiver that is configured to receive an output signal from the audio system;
a control sequencer that stores a sequence of control settings as a set of steps, each step corresponding to a first control setting for a first control of the audio system and a second control setting for a second control of the audio system; and
a processor that is configured to read control position information associated with a current step within the sequence of control settings, and to control the signal generator and the receiver in a loop such that:
upon the first control of the audio system set to a value identified by the first control setting of the current step, and upon the second control of the audio system set to a value identified by the second control setting of the current step, the signal generator provides the input signal to the audio system;
the receiver receives the output signal, wherein the output signal is captured in a manner that is associated with the current step; and
the current step is updated to a next step in the sequence of control settings,
wherein the loop repeats until stopped;
wherein:
a neural network is trained based upon the input signal, the output signal, and associated control setting values for each step in the sequence of control settings that has been processed by the loop, where the neural network corresponds to a responsive behavior of the audio system at least across the control setting values for the first control and the second control that have been processed by the loop.
9. The system of claim 8 , wherein:
the processor trains the neural network, wherein:
the processor is implemented in a select one of a computer or a dedicated hardware device.
10. The system of claim 8 , wherein:
the processor communicates the first control setting, the first output signal, the second control setting, and the second output signal, to a remote processing device; and
the neural network is trained by the remote processing device.
11. The system of claim 8 , wherein:
the sequence of control settings represents sample values from a random distribution, the sample values sorted to reduce or minimize wear of the first control and the second control.
12. The system of claim 8 , wherein a combination of control settings in the sequence of control settings represents a sufficiently high number of samples to substantially model a responsive behavior of the audio system across a range of values of the first control and the second control.
13. The system of claim 8 further comprising:
a first motor controller communicatively coupled to the processor, the first motor controller operative, responsive to an instruction from the processor, to cause a first motor to mechanically set the first control of the audio system to a value identified by the first control setting of the current step.
14. The system of claim 13 further comprising:
a second motor controller communicatively coupled to the processor, the second motor controller operative, responsive to an instruction from the processor, to cause a second motor to mechanically set the second control of the audio system to a value identified by the second control setting of the current step.
15. The system of claim 14 further comprising:
a first control coupler configured to mechanically couple the first motor to the first control of the audio system; and
a second control coupler configured to mechanically couple the second motor to the second control of the audio system.
16. The system of claim 8 , wherein the sequence of control settings comprises at least one of:
a volume control sequence comprising volume control values;
a gain control sequence comprising gain control values;
a treble control sequence comprising treble control values;
a bass control sequence comprising bass control values;
a tone control sequence comprising tone control values;
an equalizer control sequence comprising equalizer control values;
a reverb control sequence comprising reverb control values;
a contour control sequence comprising contour control values; and
a presence control sequence comprising presence control values.
17. The system of claim 8 , wherein the neural network is configured to emulate the responsive behavior of the audio system at any given control setting within a control space that is defined by the sequence of control settings.
18. A system for capturing audio systems, comprising:
a signal generator that is configured to provide an input signal to an audio system;
a receiver that is configured to receive an output signal from the audio system;
a control sequencer that stores a sequence of control settings as a set of steps, each step corresponding to a first control setting for a first control of the audio system and a second control setting for a second control of the audio system; and
a processor that is configured to read control position information associated with a current step within the sequence of control settings, and to collect responses from at least two audio systems, wherein for each audio system, the processor controls the signal generator and the receiver in a loop such that:
the first control of the audio system is set to a value identified by the first control setting of the current step;
the second control of the audio system is set to a value identified by the second control setting of the current step;
the signal generator provides the input signal to the audio system;
the receiver receives the output signal, wherein the output signal is captured in a manner that is associated with the current step; and
the current step is updated to a next step in the sequence of control settings,
wherein the loop repeats until stopped;
wherein:
a first neural network is trained for a first one of the at least two audio systems based upon the output signal and associated control setting values for each step in the sequence of control settings that has been processed by the loop for that audio system; and
a second neural network is trained for a second one of the at least two audio systems based upon the output signal and associated control setting values for each step in the sequence of control settings that has been processed by the loop for that audio system.
19. The system of claim 18 , wherein a processor compares the first neural network to the second neural network to distinguish a behavior of the first one of the at least two audio systems from the second one of the at least two audio systems.
20. The system of claim 18 , wherein:
the sequence of control settings represent sample values from a random distribution, the sample values sorted to reduce or minimize wear of the first control of the audio system and the second control of the audio system; and
a combination of control settings in the sequence of control settings processed in the loop implemented to evaluate at least the first one of the at least two audio systems represents a sufficiently high number of samples to substantially model a responsive behavior of the audio system across values of the first control and the second control of the first one of the at least two audio systems.