Recurrent Neural Network Loudspeaker Distortion Correction
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Solution Overview
Problem
Loudspeakers often exhibit nonlinearities that degrade sound quality due to factors like voice coil inductance changes, coil heating, Doppler distortion, and non-linear spring forces, which existing correction schemes fail to address effectively.
Innovation Solution
A system utilizing a recurrent neural network (RNN) with adaptive feedback to correct nonlinear distortions in loudspeakers, where the RNN receives audio input signals, outputs corrected signals, and is trained using error signals to predict and correct performance, combined with a second RNN to further refine corrections and apply non-linear distortion removal parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing physical model based or low-complexity black box model based correctors are used, then device complexity is reduced, but nonlinear distortion correction effectiveness deteriorates
Solution Approach 1:
The patent replaces traditional physical model-based correction mechanisms with a neural network-based system. The neural network learns complex nonlinear relationships between input signals and speaker distortions without requiring explicit physical models, thereby improving correction effectiveness while maintaining reasonable system complexity.
Solution Approach 2:
The patent transforms the correction approach by changing from fixed physical model parameters to adaptive neural network parameters that are trained on actual speaker distortion data. This allows the system to capture complex nonlinear behaviors that physical models cannot represent, significantly improving distortion correction effectiveness.
2Device complexity
If traditional correction schemes are used, then system simplicity is maintained, but sound quality deteriorates due to uncorrected nonlinearities
Solution Approach 1:
The patent substitutes traditional signal processing-based correction with a neural network-based correction system. The neural network processes the input signal and generates corrected output by learning from training data, achieving superior sound quality while keeping the overall system structure relatively simple.
Solution Approach 2:
The neural network correction system is trained offline on speaker-specific distortion characteristics, allowing the system to adapt to each speaker's unique nonlinearities. This self-adaptation capability improves sound quality without requiring complex real-time adjustment mechanisms.
Data Source
AI summary
An audio system is described that corrects for linear and nonlinear distortions. The system can include a physical loudspeaker system responsive to an audio input signal, an adaptive circuit, e.g., with a recurrent neural network, to correct for non-linear distortions from the loudspeaker.