Neural Network Audio System Emulation Model
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Solution Overview
Problem
Existing audio systems require extensive and time-consuming measurement of control settings to determine responsive behaviors, leading to mechanical wear and inefficiency in capturing the full range of sonic responses across various settings.
Innovation Solution
A robotic system and automated process that uses a neural network to emulate the behavior of a reference audio system by systematically varying control settings, collecting data through a robotic system with electric motors and an audio interface, and utilizing a loss function to improve model accuracy, allowing for a controllable responsive model that can mimic the audio system across different settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive measurement of control settings is performed to determine responsive behaviors, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-collecting measurement data for multiple control settings before actual use. The system performs extensive measurements in advance to build a comprehensive database of responsive behaviors, which can then be quickly queried during operation without requiring real-time measurements.
Solution Approach 2:
The patent uses copying by creating a digital model that replicates the audio system's responsive behaviors. Instead of repeatedly measuring the actual system, the patent creates a virtual copy through neural network modeling that can simulate the system's responses to different control settings, eliminating the need for time-consuming physical measurements.
2Measurement precision
If extensive measurement of control settings is performed to determine responsive behaviors, then measurement precision is improved, but mechanical wear increases
Solution Approach 1:
The patent uses copying by creating a digital model that replicates the audio system's responsive behaviors. Instead of repeatedly measuring the actual system, the patent creates a virtual copy through neural network modeling that can simulate the system's responses to different control settings, eliminating the need for time-consuming physical measurements.
Solution Approach 2:
The patent applies mechanics substitution by replacing physical measurement processes with computational modeling. The neural network model substitutes for the need to physically adjust and measure each control setting, using mathematical computations instead of mechanical operations to determine responsive behaviors.
3Productivity
If a neural network model is created to emulate audio system behavior, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent uses an intermediary approach by introducing a neural network model as a mediator between the user and the actual audio system. The model serves as an intermediate layer that handles the complexity of modeling and simulation, allowing the user to interact with a simplified interface while the neural network manages the sophisticated computations in the background.
4Measurement precision
If control settings are systematically varied to capture full range of sonic responses, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-collecting measurement data for multiple control settings before actual use. The system performs extensive measurements in advance to build a comprehensive database of responsive behaviors, which can then be quickly queried during operation without requiring real-time measurements.
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A neural network emulates a behavior of a reference audio system for at least two control settings. A process, for each control setting, receives control position data designating a select control setting of the reference audio system as conditioning for the neural network, communicates an input to the reference audio system and captures a target output, maps parameters of the neural network such that, responsive to the input, a neural output resembles the target output, scores by a loss function, a similarity of the neural network output compared to the target output of the reference audio system, and utilizes the similarity derived from the loss function to modify model parameters of the neural network. A graphical user interface enables a user to select a virtual control setting within the graphical user interface such that the neural network models the reference audio system at the corresponding control setting.