Hearing Aid Test Set Generation via Feature Selection
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Solution Overview
Problem
Current techniques for testing hearing-impaired patients to optimize hearing-enhancement system parameters are time-consuming and costly, requiring extensive testing to determine numerous parameter values, which increases exponentially with the number of system parameters.
Innovation Solution
A computer-based system that selects significant features and generates stimuli classes to efficiently assess hearing capabilities by presenting a reduced set of phonemes, using a feature-selecting module to identify vital features and a stimulus-selecting module to choose stimuli that exceed a predetermined threshold, allowing for adaptive testing and reduced testing time without compromising quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional testing techniques are used to determine all parameter values for hearing-enhancement systems, then the quality and completeness of system tuning is improved, but the testing time and cost increase exponentially
Solution Approach 1:
The patent extracts and identifies only the most significant features from the complete set of system parameters through feature selection modules. By separating critical features from less important ones, the system tests only the essential parameters that have the greatest impact on hearing enhancement performance, thereby reducing testing time while maintaining tuning quality.
Solution Approach 2:
The patent applies partial action by testing a reduced subset of parameters rather than the complete parameter set. The feature selection process determines which parameters require testing based on their significance to the specific patient's hearing characteristics, performing only the necessary portion of testing needed to achieve effective system tuning.
2Measurement precision
If the number of system parameters to be tested is increased to improve tuning accuracy, then the quality of hearing enhancement is improved, but the complexity of the testing process increases
Solution Approach 1:
The patent segments the complete parameter testing process into distinct phases: feature selection, significance determination, and selective testing. By dividing the monolithic testing process into manageable segments based on feature importance, the system maintains tuning accuracy while reducing the complexity of the overall testing procedure.
Solution Approach 2:
The patent introduces dynamic adaptability through feature selection modules that adjust which parameters are tested based on individual patient characteristics and hearing profiles. The testing process is no longer static and comprehensive but dynamically adapts to test only the parameters most relevant to each patient, reducing complexity while preserving accuracy.
3Productivity
If a reduced test set is used to decrease testing time, then the efficiency of the testing process is improved, but the quality of assessment may be compromised
Solution Approach 1:
The patent changes the parameters being tested by selecting only those features that have the greatest impact on hearing enhancement based on statistical significance and patient-specific characteristics. By transforming the parameter set from comprehensive to optimized, the system achieves both reduced testing time and maintained assessment quality through intelligent parameter selection.
Solution Approach 2:
The patent incorporates feedback mechanisms where the feature selection modules continuously evaluate the significance of each parameter based on patient responses and hearing profiles. This feedback loop ensures that only the most informative parameters are included in the reduced test set, maintaining assessment quality while improving efficiency.
Data Source
AI summary
A computer-implemented method for generating a test set for testing a subject using a computer system comprising logic-based processing circuitry is provided. The method includes the step of selecting one or more features from among a plurality of features, wherein a measurable effect on the subject of each feature selected is based on a predetermined threshold. The method also includes generating one or more classes of stimuli based on the selected features. The method further includes selecting stimuli from one or more of said classes for presenting to the subject.