Neural Network Loudspeaker Parameter Estimation
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Solution Overview
Problem
Current techniques for estimating parameter values for lumped parameter models of loudspeakers are inaccurate and impractical, often damaging the speaker or perturbing its behavior, which limits their effectiveness in high-accuracy design processes.
Innovation Solution
A computer-implemented method using a neural network model to estimate parameter values for loudspeaker models based on audio input signals and measured responses, trained on varying sets of parameter values to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If direct measurement techniques are used to obtain parameter values, then measurement speed is improved, but measurement precision deteriorates due to speaker damage or perturbation
Solution Approach 1:
The patent introduces a neural network model as an intermediary between the audio input signal and the lumped parameter values. The neural network processes the audio signal and generates accurate parameter estimates without requiring direct physical measurement that could damage or perturb the speaker, thus resolving the contradiction between fast measurement and high precision
Solution Approach 2:
The patent replaces traditional mechanical measurement systems (like Klippel Analyzer) with a computational neural network model. This substitution eliminates the need for complex physical measurement apparatus that may cause speaker damage, achieving both speed and precision through software-based parameter estimation
2Measurement precision
If Klippel Analyzer measurements are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential function of parameter measurement from complex physical measurement systems like the Klippel Analyzer. By using only a simple audio input device and a neural network model, it separates the measurement function from the complex measurement apparatus, achieving high precision with minimal device complexity
Solution Approach 2:
The patent creates a virtual model (neural network) that copies the functionality of complex measurement systems. Instead of using the actual complex hardware, the neural network learns to replicate the parameter estimation function, providing the same measurement precision with far simpler equipment
3Ease of operation
If conventional estimation techniques are used, then ease of operation is improved, but reliability deteriorates due to insufficient accuracy for high-precision design processes
Solution Approach 1:
The patent performs preliminary training of the neural network model using extensive datasets before actual parameter estimation. This preliminary action prepares the model to provide high-reliability estimates during operation, maintaining ease of use while achieving the accuracy required for sophisticated design processes like nonlinear correction
Data Source
AI summary
In one embodiment of the present invention, a loudspeaker parameter estimation subsystem efficiently and accurately estimates parameter values for a lumped parameter model (LPM) of a loudspeaker. In operation, the loudspeaker parameter estimation subsystem trains a neural network model based on responses generated via the lumped parameter model and the corresponding sets of parameter values. Subsequently, based on the relationship between the measured output response of a loudspeaker to an input stimulus, the loudspeaker parameter estimation subsystem estimates parameter values for the LPM of the loudspeaker. Advantageously, by sagaciously estimating parameter values for the LPM of loudspeakers, these NN-based techniques enable designers to leverage the LPM to reliably improve the design of loudspeakers, perform nonlinear correction of loudspeakers, and the like.