Audio Personalization Through HRTF Error Matching
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Solution Overview
Problem
Existing technologies struggle to provide personalized and immersive audio experiences for interactive content due to the unique head-related audio properties of each user, making it impractical to measure and replicate individual head-related transfer functions (HRTFs) for millions of users.
Innovation Solution
A method and system that utilizes a library of HRTFs from a diverse set of reference individuals, allowing users to calibrate their audio experience by matching sound localization errors, enabling the selection of a closest matching HRTF without direct measurement, and optionally blending HRTFs for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual HRTF measurements are performed for each user, then audio personalization accuracy is improved, but system complexity and measurement time increase significantly
Solution Approach 1:
The patent creates a library of HRTF profiles from reference individuals and uses these as templates to represent groups of users with similar audio characteristics. Instead of measuring each user individually, the system copies appropriate HRTF profiles from the reference library and applies them to users based on matching criteria, thereby achieving personalization without individual measurements.
Solution Approach 2:
The patent performs HRTF measurements and calibrations in advance for a diverse set of reference individuals, storing these results in a library. When a new user accesses the system, the pre-computed HRTF profiles are already available for rapid matching and selection, eliminating the need for time-consuming individual measurements while maintaining personalization quality.
2Measurement precision
If a large library of reference HRTFs is created, then audio personalization quality is improved, but data storage requirements and processing time increase
Solution Approach 1:
The patent segments the user population into distinct groups based on shared audio characteristics (e.g., hearing profile, head-related transfer function patterns). Each segment is represented by one or more reference HRTF profiles. This segmentation allows the system to manage large amounts of HRTF data in an organized manner and quickly identify the appropriate segment for each user without processing the entire library.
Solution Approach 2:
The patent organizes the HRTF library using multiple parameters including frequency response characteristics, spectral shape, and temporal features. By indexing and categorizing HRTF profiles according to these parameters, the system can efficiently search and match user profiles against the library using relevant parameters, reducing the effective search space and processing requirements while maintaining high personalization quality.
3Measurement precision
If calibration testing is performed for each user, then HRTF matching accuracy is improved, but user testing time and system resources increase
Solution Approach 1:
The patent implements a two-stage matching process: first, a rapid coarse matching stage uses key parameters to identify a small subset of candidate reference profiles, and second, a more detailed comparison stage refines the selection. This partial application of full calibration testing reduces user time investment while maintaining matching accuracy by focusing detailed analysis only on the most promising candidates rather than performing exhaustive comparisons with all reference profiles.
Data Source
AI summary
An audio personalisation method for a first user includes: testing a first user on a calibration test, the calibration test comprising requiring a user to match a test sound to a test location, either by controlling the position of the presented sound or controlling the position of the presented location, for a sequence of test matches, each test sound being presented at a position using a default head related transfer function ‘HRTF’, receiving an estimate of each matching location from the first user, and calculating a respective error for each estimate, to generate a sequence of location estimate errors for the first user; and comparing at least some of the location estimate errors for the first user with estimate errors of the same locations previously generated for at least a subset of a corpus of reference individuals; identifying a reference individual with the closest match of compared location estimation errors to those of the first user; and using an HRTF, previously obtained for the identified reference individual, for the first user.