3D Head Shape Cleanup for Accurate Individualized HRTF Synthesis
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Solution Overview
Problem
Existing 3D scanning techniques for generating head-related transfer functions (HRTFs) are prone to distortion due to acoustically irrelevant surfaces like hair, requiring manual intervention which is error-prone and impractical for large user bases, especially in consumer products.
Innovation Solution
A machine learning-based approach using supervised and unsupervised learning to generate accurate 3D head models by removing acoustically irrelevant surfaces, such as hair, through a GAN model trained on anatomical constraints and heuristics, ensuring precise HRTF synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D scanning techniques are used to generate head-related transfer functions, then individualized HRTF can be obtained, but distortion occurs due to acoustically irrelevant surfaces like hair
Solution Approach 1:
The patent extracts and removes acoustically irrelevant surfaces (such as hair) from the 3D scanned head model while preserving the acoustically relevant head geometry. This extraction process eliminates the distortion caused by hair surfaces that do not interact with sound waves, thereby improving HRTF synthesis reliability without sacrificing measurement precision
Solution Approach 2:
The patent introduces an intermediary processing step between 3D scanning and HRTF synthesis that identifies and separates acoustically relevant from irrelevant surfaces. This intermediary layer acts as a filter that prepares the scanned data by removing problematic elements like hair, enabling accurate HRTF generation from consumer-grade scans
2Reliability
If manual removal of hair surfaces is performed, then HRTF distortion can be reduced, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of hair removal with an automated computational algorithm. The system uses image processing and geometric analysis to automatically identify and remove hair surfaces from 3D scans, eliminating the need for manual intervention while maintaining high reliability and reducing processing time significantly
Solution Approach 2:
The patent enables the system to perform hair removal automatically without human assistance. The algorithm autonomously analyzes the 3D scan data, identifies acoustically irrelevant surfaces based on geometric and textural characteristics, and removes them, making the entire process self-service and suitable for large-scale consumer applications
3Productivity
If consumer products use individualized HRTF for large user bases, then audio quality improves, but manual processing becomes impractical
Solution Approach 1:
The patent creates a universal automated processing system that can handle diverse 3D head scans from different consumers using a single standardized algorithm. The system is designed to process various hair types, densities, and styles uniformly, making it easy to deploy across large user bases in consumer products without requiring specialized manual processing for each user
Solution Approach 2:
The patent replaces impractical manual processing with an automated computational system that can rapidly process individualized HRTF data for large numbers of users. This substitution enables consumer products to generate accurate, personalized HRTFs at scale, dramatically improving productivity while maintaining ease of operation through fully automated workflows
Data Source
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AI summary
Disclosed embodiments include techniques for generating a clean 3D shape of a portion of the human head of a subject from a 3D scan that includes overlaying surfaces that occlude a portion of the head, where the overlaying surfaces where, the overlaying surfaces are acoustically transparent, such as human hair. These techniques use heuristic constraints to generate a clean 3D shape of a human head for the purpose of generating a more realistic head-related transfer function (HRTF) that is individualized for the subject. The improved HRTF, when applied to a sound system, leads to a more immersive and realistic acoustic experience for the subject when listening to music, experiencing a virtual reality experience, and/or the like.