Hearing Aid Parameter Adjustment via Thompson Sampling
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Solution Overview
Problem
Current hearing aids often require extensive initial fitting sessions and lack personalization, leading to suboptimal performance and user dissatisfaction due to insufficient user interaction and adaptation to changing sound environments.
Innovation Solution
A hearing aid system with an adjustment processor that calculates and adjusts signal processing parameters using Thompson sampling and Bayes rule, allowing users to provide feedback through minimal input to optimize settings in real-time, balancing exploration and exploitation to improve sound quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hearing aids use fixed signal processing parameters determined during initial fitting, then device complexity is reduced, but adaptability to changing sound environments and user preferences deteriorates
Solution Approach 1:
The patent implements dynamic adjustment of signal processing parameters by allowing users to provide feedback (dissent/consent inputs) that trigger recalculation of parameters using Thompson sampling and Bayes rule. The system transitions from static initial fitting parameters to dynamically adapted parameters that evolve based on user responses, resolving the contradiction between fixed simplicity and adaptive complexity.
Solution Approach 2:
The hearing aid system performs self-adjustment by automatically recalculating signal processing parameters based on user feedback without requiring audiologist intervention. The adjustment processor uses Bayesian optimization to autonomously adapt parameters to user preferences and environmental conditions, enabling the device to serve itself in optimizing its performance.
2Manufacturing precision
If hearing aids require extensive initial fitting sessions with audiologists, then manufacturing precision of parameter settings is improved, but loss of time for users increases
Solution Approach 1:
The system performs preliminary parameter optimization by implementing Thompson sampling during the initial fitting to generate a preference probability distribution. This preliminary action creates a foundation for rapid subsequent adjustments, reducing the need for extended fitting sessions while maintaining precision through automated Bayesian optimization.
Solution Approach 2:
The patent implements continuous feedback loops where user responses (dissent/consent inputs) are used to update the preference probability distribution and recalibrate parameters. This feedback mechanism enables the system to achieve high parameter precision through iterative learning rather than extensive initial manual fitting, significantly reducing time loss.
3Adaptability or versatility
If hearing aids use automated parameter adjustment with user feedback, then adaptability to user preferences is improved, but device complexity increases
Solution Approach 1:
The patent introduces an adjustment processor as an intermediary component that handles the complex Bayesian optimization calculations. This intermediary separates the complex adaptive logic from the core hearing aid signal processing, enabling personalization capability while managing device complexity through modular architecture.
Solution Approach 2:
The system achieves personalization by dynamically changing signal processing parameters (compression ratios, attack/release times, frequency responses) based on user feedback. The adjustment processor modifies these parameters iteratively using Thompson sampling, enabling adaptability to individual user preferences while containing complexity through focused parameter optimization.
4Adaptability or versatility
If hearing aids perform frequent parameter recalculation, then adaptability to changing environments is improved, but use of energy increases
Solution Approach 1:
The patent implements periodic parameter recalculation triggered by user feedback events rather than continuous computation. The system uses event-driven architecture where Thompson sampling and Bayes rule updates occur only when dissent/consent inputs are received, achieving real-time adaptability while minimizing energy consumption by avoiding unnecessary processing.
Solution Approach 2:
The system performs partial recalculation by updating only the preference probability distribution and affected parameters rather than complete system reconfiguration. This partial action approach enables real-time adaptation to user preferences while reducing computational overhead and energy consumption compared to full parameter sets.
Data Source
AI summary
A hearing aid system is provided that facilitates adjustment of signal processing parameters θ of the hearing aid system with minimum user intervention, wherein the hearing aid system is capable of calculating signal processing parameters θ for evaluation of the user when the user has entered an input, e.g. using a smartwatch, to this effect. The evaluation takes place for a certain time period and in the event that the user has entered a consent input indicating that he or she is pleased with the set θ of signal processing parameters under evaluation, the hearing aid system continues processing with those signal processing parameters; and if the user is not pleased with the signal processing parameters θ under evaluation, the hearing aid system calculates another set {circumflex over (θ)} of signal processing parameters for evaluation of the user.