Hearing Device Supplementary Sound Classes for User-Specific Parametrization
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Solution Overview
Problem
Hearing devices often require frequent adjustments by users to adapt to changing sound environments, leading to unspecific modifications that can affect other sound situations negatively and decrease user satisfaction.
Innovation Solution
A method that automatically generates supplementary sound classes based on collected user adjustments, allowing for interpolation between basic and supplementary sound classes to optimize actuator parametrizations, reducing the need for frequent user adjustments and improving sound classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually adjust hearing device parameters frequently to adapt to changing sound environments, then the hearing device can be customized to user preferences, but this leads to unspecific modifications that affect other sound situations negatively and decrease user satisfaction
Solution Approach 1:
The hearing device automatically generates supplementary sound classes by analyzing user adjustment patterns and classifying sound situations. The system serves itself by autonomously creating and optimizing actuator parametrizations without requiring manual user intervention, thereby maintaining adaptability while preventing unspecific modifications.
Solution Approach 2:
The system collects data on user adjustments and sound situations, analyzes this feedback to identify patterns, and uses this information to automatically generate supplementary sound classes. This closed-loop feedback mechanism ensures that modifications are specific and targeted, affecting only the relevant sound situations while preserving other sound quality.
2Ease of operation
If the hearing device requires frequent user adjustments to optimize performance in different sound environments, then user satisfaction can be improved through customization, but this increases the complexity of operation and time required for adjustment
Solution Approach 1:
The hearing device pre-generates supplementary sound classes based on collected adjustment data before the user needs to make manual adjustments. By performing this action in advance, the system eliminates the need for frequent user interventions and reduces adjustment time while maintaining high user satisfaction through optimized customization.
Solution Approach 2:
The system automatically performs the customization process that would otherwise require user intervention. By analyzing adjustment patterns and autonomously generating optimized sound classes, the hearing device serves itself, eliminating time loss while preserving ease of operation and user satisfaction.
3Manufacturing precision
If manual adjustment of actuator parameters is performed to optimize sound quality, then specific sound situations can be improved, but this creates a complex adjustment process that may not systematically address all sound environments
Solution Approach 1:
The manual mechanical adjustment process is replaced with an automated computational system. The hearing device uses algorithms to analyze adjustment data, classify sound situations, and generate optimized actuator parametrizations automatically. This substitution maintains the precision of manual adjustment while eliminating the complexity of the adjustment process.
Solution Approach 2:
The system implements a feedback loop that continuously collects adjustment data, analyzes it to identify patterns, and automatically generates optimized sound classes. This feedback mechanism ensures systematic coverage of all sound environments while maintaining high precision in actuator parametrization without requiring complex manual adjustment procedures.
4Adaptability or versatility
If basic sound classes are used to cover all sound situations, then the hearing device can handle diverse environments, but this may not capture user-specific preferences and systematic adjustment patterns
Solution Approach 1:
The hearing device segments the basic sound classes by analyzing user adjustment patterns and creating supplementary sound classes for specific sound situations. This segmentation allows the system to maintain broad coverage of diverse environments while adding precision for user-specific preferences and systematically adjusted sound classes.
Solution Approach 2:
The sound class structure is made dynamic by automatically generating supplementary classes based on collected adjustment data. Rather than using a static set of basic sound classes, the system continuously adapts and expands its classification system to capture user-specific preferences and systematic adjustment patterns, improving measurement precision while maintaining versatility.
Data Source
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AI summary
A method for adjusting at least one hearing device (12) comprises: providing the at least one hearing device (12) with basic sound classes (26), each basic sound class (26) comprising an actuator parametrization (42) with parameters for at least one actuator (32) of the hearing device (12); collecting of adjustments (36) of sound properties of at least one user of the at least one hearing device (12) together with weightings (40) of a sound signal (22) acquired by the hearing device (12) at which the adjustments (36) have been made; analyzing the collected adjustments (36), whether same adjustments (36) have been applied at same weightings (40); generating at least one supplementary sound class (28), when the same adjustments (36) have been applied at a weighting (44), wherein the actuator parametrization (42) of the supplementary sound class (28) is a modified actuator parametrization based on the adjustments (36) at the weighting (44).