Hearing Profile Selection Using Audio Samples and Demographic Clustering
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Solution Overview
Problem
Current methods for generating personalized hearing profiles are inefficient, costly, and cumbersome, often relying on extensive user input or complex testing procedures that are unreliable and not suitable for mass deployment, and they require significant data processing workload in audio source networks.
Innovation Solution
A method that selects an initial hearing profile based on demographic factors such as age and gender, identifies alternate profiles with varying coefficients for multiband compression, and allows users to select their preferred profile through audio samples, reducing the need for extensive user input and data processing by using a client-server architecture or web-based system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hearing profile generation methods using detailed audiometric testing are employed, then measurement precision and reliability of hearing profiles are improved, but device complexity, time consumption, and cost increase significantly
Solution Approach 1:
The patent creates simplified copies of professional hearing profiles by using audio sample preference data to generate representative hearing profiles through clustering algorithms, rather than requiring actual audiometric testing equipment and procedures
Solution Approach 2:
The patent replaces the mechanical audiometric testing system with an information-processing system that uses psychological preference surveys and statistical clustering to generate hearing profiles, eliminating the need for specialized testing equipment
2Reliability
If comprehensive audiometric testing with sophisticated equipment is used, then hearing profile reliability is improved, but ease of operation and accessibility deteriorate
Solution Approach 1:
The system generates reliable hearing profiles by creating statistical representations from preference data, copying the essential characteristics of professional profiles without requiring professional testing environments
Solution Approach 2:
Users can generate their own hearing profiles by completing simple preference surveys on their personal devices, eliminating the need to visit audiologists or specialized testing centers
3Loss of information
If detailed user questionnaires with many questions are employed, then information completeness for profile generation is improved, but user completion rate and ease of operation decrease
Solution Approach 1:
The patent uses a limited set of preference questions that capture sufficient information for clustering, recognizing that complete audiometric data is unnecessary when using preference-based psychological testing to generate representative profiles
Solution Approach 2:
The system uses clustering algorithms to identify patterns in preference data, providing implicit feedback that validates whether sufficient information has been collected to generate reliable profile groups
4Measurement precision
If traditional hearing profile methods are used, then profile accuracy is improved, but productivity and speed of profile generation deteriorate
Solution Approach 1:
The patent pre-computes cluster centroids and profile representations offline, so that when users complete preference surveys, their profiles can be rapidly generated by matching responses to pre-established clusters rather than performing complex real-time analysis
Solution Approach 2:
The system rapidly generates profiles by copying and adapting pre-established cluster profiles to match user preferences, rather than creating entirely new profiles through time-consuming analysis
5Adaptability or versatility
If mass personalization efforts are implemented, then adaptability to individual hearing needs is improved, but data processing workload and system complexity increase
Solution Approach 1:
The patent segments the continuous space of hearing profiles into discrete clusters, allowing mass personalization through simple cluster assignment rather than managing the full complexity of individual profile variations across large populations
Solution Approach 2:
The system changes the parameter space from continuous audiometric measurements to discrete preference categories and cluster assignments, simplifying data processing while maintaining personalization capability
Data Source
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.