Hearing Aid Personalization via Gaussian Process Optimization
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Solution Overview
Problem
Existing hearing aid systems face challenges in personalization due to difficulty in user preference elicitation, high processing and memory requirements, and the need for complex user interaction, which limits ease of use and user satisfaction.
Innovation Solution
A method for optimizing hearing aid system settings using a user-preference elicitation method that prompts users to compare and rate sound settings, employing analytical expressions and Gaussian processes to derive personalized settings efficiently, even with limited processing resources, and allowing for pseudo-random variation of sound parameters to improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex user preference elicitation methods are used to achieve accurate personalization, then personalization accuracy is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The personalization process is segmented into multiple iterations, each evaluating a small number of parameters (e.g., 2-5 parameters per iteration) rather than all parameters at once. This divides the complex personalization task into manageable segments that are easier for users to evaluate and for the system to process
Solution Approach 2:
The system performs preliminary actions by automatically selecting parameter values and generating sound settings before user evaluation. The hearing aid system pre-processes parameter combinations and prepares evaluation sounds, reducing the cognitive load on users and simplifying the interaction process
2Measurement precision
If comprehensive user preference elicitation is performed to achieve accurate personalization, then personalization accuracy is improved, but loss of time increases
Solution Approach 1:
The personalization process is made dynamic and adaptive, adjusting the number of iterations and parameters evaluated based on user responses and convergence criteria. The system can terminate the process early if satisfactory personalization is achieved, or continue if more refinement is needed, optimizing the time invested
Solution Approach 2:
The system implements feedback mechanisms where user responses are continuously incorporated to refine parameter selections. This feedback loop allows the system to learn from user preferences and converge on optimal settings more efficiently, reducing the total number of iterations required
3Productivity
If analytical expressions and Gaussian processes are used to optimize parameter selection, then productivity of personalization is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary optimization module that acts as a mediator between the user interface and the hearing aid processing. This module handles the complex analytical expressions and Gaussian process computations, shielding the user from complexity while maintaining high productivity through efficient mathematical optimization
Data Source
AI summary
A hearing aid system (100) adapted to provide improved user personalization and a method of operating such a hearing aid system.