Audio System Emulation Using Random Sampling and Virtual Controls
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Solution Overview
Problem
Conventional systems require impractically large numbers of snapshots and extensive time to accurately emulate the behavior of audio systems, leading to mechanical wear and excessive data storage needs, making comprehensive digital emulation of audio systems inefficient and impractical.
Innovation Solution
A digital model of an audio system is created through sufficiently dense random sampling, allowing a single model to emulate the behavior across various settings, reducing the need for extensive data capture and storage, and enabling dynamic adjustment of virtual controls that mimic physical controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional snapshot methods are used to capture audio system behavior, then emulation accuracy is improved, but data storage requirements and capture time increase excessively
Solution Approach 1:
The patent creates a digital copy of the audio system's behavior through a trained neural network model that learns from a limited set of actual snapshots. This digital copy can then emulate the audio system across all possible settings without requiring exhaustive sampling, thus reducing capture time while maintaining emulation accuracy.
Solution Approach 2:
The patent performs preliminary training of the neural network model using a dense random sampling of snapshots before actual use. This preliminary action creates a pre-trained model that can quickly respond to user interactions without requiring real-time capture or processing of additional snapshots, thereby reducing operational capture time.
2Measurement precision
If conventional snapshot methods are used to capture audio system behavior, then emulation accuracy is improved, but storage space requirements increase excessively
Solution Approach 1:
Instead of storing numerous actual snapshots, the patent creates a compressed digital representation through a trained neural network model. This model encapsulates the essential behavior patterns of the audio system across multiple settings in a compact form, dramatically reducing storage requirements while preserving emulation accuracy.
Solution Approach 2:
The patent transforms the raw snapshot data into a different parameter space through neural network training. The model learns to represent audio system behavior in terms of learned parameters and weights rather than storing complete snapshot recordings, achieving efficient compression while maintaining the ability to accurately emulate various settings.
3Measurement precision
If exhaustive sampling of all settings is performed, then emulation accuracy is improved, but mechanical wear on physical controls increases
Solution Approach 1:
The patent creates a virtual copy of the audio system's control interface through the neural network model. Users can interact with virtual controls that trigger pre-computed emulations without physically touching the original controls during the sampling phase, thereby preventing mechanical wear while still achieving accurate emulation through the trained model.
Solution Approach 2:
The patent performs the exhaustive sampling and model training in advance during a setup phase, after which the system can operate indefinitely without further physical interaction with the original controls. This preliminary action concentrates the mechanical wear to a controlled initial period while enabling long-term wear-free operation.
4Device complexity
If a single digital model emulates multiple settings, then device complexity is reduced, but the difficulty of capturing and training the model increases
Solution Approach 1:
The patent replaces complex mechanical control systems with a neural network-based digital model that uses learned patterns to emulate different settings. The neural network substitutes for multiple physical control mechanisms, achieving a unified model structure that manages complexity through software intelligence rather than hardware complexity.
Solution Approach 2:
The patent creates a universal digital model that can emulate multiple different audio system settings through a single neural network architecture. This universal model uses learned representations that generalize across different parameter combinations, allowing one model to perform the function of what would traditionally require multiple separate models or control systems.
Data Source
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AI summary
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.