Loudspeaker Neural Network Control for Precise Displacement
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Solution Overview
Problem
Conventional solutions for nonlinear control of loudspeakers are complex, difficult to implement, and limited in precision due to insufficient physical models that fail to capture the dynamic, nonlinear, and time-varying relationship between voltage and displacement of moving components, requiring specialized equipment and expertise.
Innovation Solution
A neural network is trained to learn the mapping between voltage and displacement of loudspeaker moving components, allowing for the determination of an input control voltage to achieve a target displacement during audio reproduction, simplifying the control process and eliminating the need for complex physical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional physical models are used for nonlinear control of loudspeakers, then the control system can be implemented with traditional methods, but the precision is limited and the system becomes complex due to insufficient models that fail to capture dynamic nonlinear relationships
Solution Approach 1:
The patent replaces traditional mechanical/physical modeling approaches with a neural network-based computational model. The neural network learns the complex nonlinear relationship between voltage and displacement directly from data, substituting the need for complex physical models and specialized measurement equipment. This achieves higher precision control while simplifying the system implementation.
Solution Approach 2:
The patent transforms the control approach by changing from fixed physical model parameters to adaptive neural network parameters that are trained on experimental data. The neural network parameters automatically adjust to capture the dynamic nonlinear characteristics of the loudspeaker, improving precision without requiring complex manual modeling.
2Ease of manufacture
If conventional nonlinear control methods are implemented, then traditional equipment can be used, but specialized equipment and expertise are required and the setup becomes difficult
Solution Approach 1:
The neural network model performs self-learning by automatically capturing the nonlinear relationships between voltage and displacement through training data. This self-service capability eliminates the need for specialized equipment and expert knowledge to create and maintain complex physical models, making the system easier to set up and operate.
Solution Approach 2:
The patent creates a virtual copy of the loudspeaker's nonlinear behavior through the neural network model. This digital twin captures the complex dynamics without requiring physical measurement equipment during operation, simplifying both setup and ongoing use while maintaining high precision.
Data Source
AI summary
One embodiment provides a method comprising inputting into a first layer of a neural network at least one data sample of a current displacement of one or more moving components of a loudspeaker and at least one or more other data samples of one or more prior displacements of the one or more moving components. The method comprises generating in the first layer a data vector by applying a nonlinear activation function based on the at least one data sample of the current displacement and the at least one or more other data samples of the one or more prior displacements. The method comprises inputting into a second layer of the neural network the data vector and applying an affine transformation to the data vector. The method comprises outputting a prediction of a current voltage to apply to the loudspeaker based on the affine transformation applied to the data vector.