Hearing Aid Feedback Cancellation Configuration Prediction
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Solution Overview
Problem
Hearing aids often experience acoustic feedback issues due to the amplification of output sounds by microphones, leading to undesirable whistling or squealing, which existing configuration methods may not adequately address, particularly during the fitting process.
Innovation Solution
A method and system that predict initial feedback cancellation configurations for hearing aids based on the properties of similar hearing aids, allowing for more accurate parameter settings and reduced fitting time by using a programmer to determine and implement predicted bulk delay values and other parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feedback cancelation configuration methods are used, then the hearing aid may be programmed with default or manually configured parameters, but this results in suboptimal feedback cancelation performance and requires time-consuming manual adjustment by clinicians
Solution Approach 1:
The system performs preliminary configuration actions by predicting optimal feedback cancelation parameters (bulk delay values, filter coefficients) based on hearing aid properties and stored configuration data from similar devices, allowing the hearing aid to be pre-configured before actual use, thereby reducing fitting time while maintaining accuracy
Solution Approach 2:
The system copies successful feedback cancelation configurations from a database of previously configured hearing aids with similar properties, adapting these proven configurations to the current device, which eliminates time-consuming manual tuning while preserving effective parameter settings
2Adaptability or versatility
If manual configuration by clinicians is used, then some customization is possible, but this leads to variability in configuration quality and increases the complexity of the fitting process
Solution Approach 1:
The hearing aid system performs self-configuration by automatically predicting optimal parameters based on its own properties and stored data, eliminating the need for complex manual intervention while maintaining adaptability to different hearing aid models and properties
Solution Approach 2:
The system automatically adjusts key parameters such as bulk delay values and filter coefficients based on hearing aid properties and stored configuration patterns, providing adaptive configuration without requiring manual parameter tuning, thus reducing complexity while preserving versatility
3Ease of operation
If generic feedback cancelation settings are applied, then the configuration process is simplified, but this results in suboptimal performance for specific hearing aid models and properties
Solution Approach 1:
The system applies localized optimization by predicting configuration parameters specific to each hearing aid model and property combination rather than using uniform generic settings, ensuring optimal performance for each specific device while maintaining ease of configuration through automated prediction
Data Source
AI summary
In one example, a method includes determining, by one or more processors of a hearing aid programmer, values for one or more properties of a first hearing aid; obtaining, by the one or more processors, based on feedback cancelation configurations of a plurality of other hearing aids having the same values for the one or more properties as the first hearing aid, a predicted initial feedback cancelation configuration for the first hearing aid; and programming, by the one or more processors, the first hearing aid based on the predicted initial feedback cancelation configuration.