Loudspeaker Audio Compensation Using DNN Feedback for Nonlinear Distortion

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Solution Overview

Problem

Loudspeakers suffer from nonlinear and time-varying distortions that traditional linear digital filters fail to adequately compensate for, leading to poor audio waveform conversion and potential damage due to overheating.

Innovation Solution

Employing a deep neural network (DNN) to modify audio signals based on feedback parameters such as loudspeaker temperature and environmental conditions, optimizing the signal to compensate for nonlinear and time-varying distortions while protecting the loudspeaker from damage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional linear digital filters are used to compensate for loudspeaker distortions, then the system complexity is low, but the compensation effectiveness for nonlinear and time-varying distortions is insufficient

Engineering Contradiction:

Improvecompensation effectivenessVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional linear digital filter mechanisms with a deep neural network system. The DNN learns nonlinear mappings between input audio signals and required compensation signals, enabling effective compensation for nonlinear and time-varying distortions that linear filters cannot address. This substitution of mechanical/mathematical filtering with intelligent learning systems resolves the contradiction between compensation effectiveness and system complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the static parameters of traditional filters into dynamic, adaptive parameters through the DNN. The neural network continuously adjusts its internal parameters (weights and biases) based on learned patterns from training data, allowing the system to adapt to time-varying distortions. This parameter transformation enables the system to achieve high compensation effectiveness while managing complexity through learned parameter optimization.

Inventive Principle:

Principle #35Parameter changes

2Power

If higher power is delivered to the loudspeaker to improve audio output, then the audio quality improves, but the risk of overheating and damage increases

Engineering Contradiction:

Improveoutput powerVSAvoidoverheating risk

Core Design Contradiction:

PowerVSObject-affected harmful factors

Solution Approach 1:

The patent implements a feedback mechanism where the DNN receives information about the loudspeaker's actual performance and environmental conditions. By continuously monitoring and adjusting the compensation signal based on feedback, the system can optimize power delivery to maximize audio quality while preventing conditions that lead to overheating and damage. This closed-loop control resolves the contradiction between power output and safety.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent applies preliminary action by pre-training the DNN with extensive data about loudspeaker behavior, distortion patterns, and safety thresholds. Before actual operation, the neural network learns optimal compensation strategies and safety boundaries, enabling it to proactively adjust power delivery to prevent overheating while maintaining high audio quality. This preliminary learning phase embeds safety constraints into the system's decision-making process.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260019745A1Methods and electronic devices

Publication Date: 2026.01.15 SONY GROUP CORP

AI summary

A method comprising modifying an input audio signal (uplayback(t), splayback(n)) to obtain a modified audio signal (uDNN(t), sDNN(n)) to compensate for nonlinear and/or time-varying distortions effected by a loudspeaker.