Hearing Profile Selection Using Demographic Audio Preference Matching
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Solution Overview
Problem
Current methods for generating personalized hearing profiles are inefficient, costly, and cumbersome, often relying on complex questionnaires or testing procedures that yield unreliable results, making mass deployment impractical.
Innovation Solution
A system that selects a user's initial hearing profile based on demographic data such as age and gender, using a database of hearing profiles to generate alternate profiles and presenting sound samples for user preference, reducing the need for extensive user input and computational workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed audiometric testing with sophisticated equipment is used, then hearing profile accuracy is improved, but testing time and cost increase significantly
Solution Approach 1:
The system pre-generates multiple candidate hearing profiles based on user-provided demographic information (age, gender, occupation) before the actual hearing test. This preliminary action allows the system to narrow down the search space from thousands of possible profiles to a manageable set of candidates, significantly reducing the time required for accurate measurement while maintaining precision.
Solution Approach 2:
The hearing profile generation process is segmented into multiple stages: (1) initial demographic data collection, (2) candidate profile generation from database, (3) iterative testing and elimination, and (4) final profile selection. This segmentation allows the system to achieve accurate measurements without requiring the full complexity of traditional audiometric testing at each stage.
2Reliability
If comprehensive questionnaires with many questions are used, then user input reliability is improved, but user completion rate decreases
Solution Approach 1:
The system uses only the essential demographic parameters (age, gender, occupation) needed to generate candidate profiles, rather than requiring comprehensive questionnaire responses. This partial action approach maintains sufficient reliability for profile generation while dramatically improving ease of operation and user completion rates.
Solution Approach 2:
The system introduces an intermediary database of pre-established hearing profiles that bridges the gap between minimal user input and accurate hearing profile generation. Instead of requiring users to provide extensive direct information, the system uses demographic data to select from pre-analyzed profiles, maintaining reliability while reducing operational burden.
3Ease of operation
If N-Alternative Forced Choice testing method is used, then user burden is reduced, but profile detail and accuracy are insufficient
Solution Approach 1:
The system performs preliminary generation of multiple candidate profiles with full detail before presenting them to the user for selection. This preliminary action ensures that even though the user burden is reduced to simple selection, the underlying profiles contain comprehensive detail and accuracy for personalized audio processing.
Solution Approach 2:
The system dynamically adapts the testing process by iteratively eliminating candidate profiles based on user preferences, focusing subsequent testing on the most promising candidates. This dynamic approach maintains measurement precision while keeping user burden low, as the system learns from each user response and narrows the search space.
4Measurement precision
If multiple candidate profiles are generated and tested iteratively, then final profile accuracy is improved, but computational workload increases
Solution Approach 1:
The computational process is segmented into efficient stages: demographic data processing to generate initial candidates, iterative user feedback processing to eliminate candidates, and final profile selection. Each stage processes only the necessary subset of data, reducing overall computational workload while maintaining accuracy through systematic elimination.
Solution Approach 2:
The system generates a limited number of candidate profiles (e.g., 3-5 candidates) rather than exhaustively processing all possible profiles in the database. This partial action approach provides sufficient accuracy for personalized audio processing while keeping computational workload manageable for mobile and portable devices.
Data Source
Figure 1A~1B
Figure 2A
Figure 2B
AI summary
A method of generating a personalized sound system hearing profile for a user. The method begins by selecting an initial profile, based on selected factors of user input. In an embodiment, the initial profile is selected based on demographic factors. Then the system identifies one or more alternate profiles, each having a selected relationship with the initial profile. The relationship between alternate profiles and the initial profile can be based on gain as a function of frequency, one alternate profile having a higher sensitivity at given frequencies and the other a lower sensitivity. The next step links at least one audio sample with the initial and alternate profiles and then plays the selected samples for the user. The system then receives identification of the preferred sample from the user; and selects a final profile based on the user's preference. An embodiment offers multiple sound samples in different modes, resulting in the selection of multiple final profiles for the different modes. Finally, the system may apply the final profile to the sound system.