Iterative HRTF Refinement via User Feedback
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Solution Overview
Problem
Existing audio systems in artificial reality struggle to accurately calculate head-related transfer functions (HRTFs) for users, leading to inconsistencies in sound presentation, as they rely on static estimates without active or passive user feedback, which can result in inaccurate sound localization.
Innovation Solution
An iterative process is employed where an audio system generates an initial set of HRTFs using machine learning and computer vision, and then refines them through user feedback by presenting test sounds at specific locations, adjusting test locations based on user responses, until a threshold accuracy is achieved or a set period expires.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static estimated HRTFs are used without user feedback, then computational demands and time are reduced, but sound localization accuracy deteriorates
Solution Approach 1:
The system implements an iterative feedback mechanism where user responses to test sounds are collected and used to refine HRTF estimates. The process presents test sounds at specific locations, monitors user responses (such as gaze direction or head movement), and uses this feedback to update the HRTF model, progressively improving sound localization accuracy
Solution Approach 2:
The HRTF estimation process transitions from a static approach to a dynamic iterative process. The system adapts the HRTF model in real-time based on user feedback, adjusting test locations and sound parameters across multiple iterations to optimize accuracy while managing computational resources
2Measurement precision
If iterative refinement with user feedback is implemented, then HRTF accuracy is improved, but computational demands and time increase
Solution Approach 1:
The HRTF estimation process is divided into discrete iterations, each focusing on specific aspects of the transfer function. Test locations are selected and processed in sequential batches, allowing the system to manage computational complexity by breaking down the overall task into smaller, manageable segments
Solution Approach 2:
The system dynamically adjusts parameters such as test location selection, sound frequency content, and presentation intensity across iterations. By changing these parameters based on previous iteration results, the system optimizes the balance between accuracy improvement and computational efficiency
Data Source
AI summary
An audio system generates customized head-related transfer functions (HRTFs) for a user. The audio system receives an initial set of estimated HRTFs. The initial set of HRTFs may have been estimated using a trained machine learning and computer vision system and pictures of the user's ears. The audio system generates a set of test locations using the initial set of HRTFs. The audio system presents test sounds at each of the initial set of test locations using the initial set of HRTFs. The audio system monitors user responses to the test sounds. The audio system uses the monitored responses to generate a new set of estimated HRTFs and a new set of test locations. The process repeats until a threshold accuracy is achieved or until a set period of time expires. The audio system presents audio content to the user using the customized HRTFs.