Hearing Aid Signal Processing Using Audible Contrast Threshold
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Solution Overview
Problem
Current hearing aid fitting methods rely solely on pure-tone audiograms for amplification, failing to address individual hearing-in-noise difficulties, leading to suboptimal settings for users with varying noise performance needs.
Innovation Solution
A method utilizing Audible Contrast Threshold (ACT) data, combined with audiograms and age parameters, to determine personalized signal processing settings for hearing aids, including features like directional microphones and noise reduction, through a prescription protocol based on linear regression models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If default settings of help-in-noise features are prescribed to all users, then the fitting process is simple and quick, but the hearing-in-noise performance is suboptimal for users with specific needs
Solution Approach 1:
The patent applies preliminary action by measuring speech-in-noise performance beforehand using quick speech-in-noise tests before fitting the hearing aid. This allows the system to predict individual hearing-in-noise needs in advance, enabling personalized prescription of help-in-noise features rather than using default settings for all users.
Solution Approach 2:
The patent changes the prescription parameters by introducing speech-in-noise test results and derived metrics (like predicted SRTimprovement) as new basis for setting help-in-noise features. This transforms the fitting approach from using only pure-tone thresholds to incorporating actual speech-in-noise performance measurements, allowing customized parameter adjustment for each user.
2Reliability
If speech-in-noise testing is implemented to personalize help-in-noise features, then hearing-in-noise performance improves, but the fitting process becomes more complex and time-consuming
Solution Approach 1:
The patent uses preliminary quick speech-in-noise tests that can be administered before the fitting session. The results are processed offline to derive predictive metrics, so the actual fitting process doesn't require complex real-time testing, reducing on-site complexity while maintaining personalized performance.
Solution Approach 2:
The patent introduces an intermediary computational model that translates speech-in-noise test results into predicted hearing aid performance metrics. This intermediary layer automatically processes the test data and generates prescription recommendations, reducing the need for complex manual adjustments and professional expertise during the fitting process.
3Ease of operation
If only pure-tone audiograms are used for fitting, then the fitting process is straightforward, but individual hearing-in-noise difficulties are not addressed
Solution Approach 1:
The patent segments the hearing assessment into two independent parts: pure-tone audiometry for detecting hearing thresholds and quick speech-in-noise tests for assessing speech understanding in noise. This segmentation allows both types of measurements to be taken separately and their results combined, preserving the simplicity of pure-tone testing while adding valuable speech-in-noise information.
Solution Approach 2:
The patent makes the fitting process multi-functional by using it to determine both amplification needs (from pure-tone thresholds) and help-in-noise feature settings (from speech-in-noise performance). This universal approach allows a single fitting session to address multiple hearing loss aspects simultaneously, eliminating the need for separate specialized assessments.
Data Source
AI summary
Disclosed herein are embodiments of a method of setting signal processing parameters for a hearing aid. The method can utilize Audible Contrast Threshold (ACT) data, along with a user's audiogram and age parameter, for determining hearing aid signal processing parameter settings. Further, in certain examples a prescription protocol can be used.