HRTF Personalization via Vertical-Lateral Component Segmentation
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Solution Overview
Problem
Conventional methods for measuring individualized Head-Related Transfer Functions (HRTFs) are costly and time-consuming, often resulting in unnatural sound localization due to the need for extensive measurements, and lack a simple representation of perceptually-relevant features using a small number of parameters.
Innovation Solution
The approach involves a listener-specific component for vertical variations and a general component for lateral variations in HRTFs, using a database of individual-specific vertical variations derived from a plurality of HRTFs, and employing spherical harmonic representations to estimate sectoral coefficients via Bayesian estimation, allowing for efficient modeling and estimation from a limited number of spatial samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to measure individualized HRTFs with extensive spatial measurements, then measurement precision is improved, but loss of time and cost increase significantly
Solution Approach 1:
The HRTF measurement process is segmented into two components: listener-specific vertical variations and general lateral variations. This segmentation allows the measurement system to focus on capturing only the essential listener-specific characteristics rather than requiring complete spatial measurements at numerous locations.
Solution Approach 2:
The patent extracts and isolates the listener-specific vertical variations from the complete HRTF, separating them from the general lateral variations. This extraction enables the system to use a reduced set of spatial measurements while still achieving accurate individualized HRTF representation.
2Productivity
If spherical speaker arrays are used to make measurements more rapidly, then productivity is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces expensive spherical speaker arrays with simpler, more affordable movable speaker arrays. While the simpler arrays require longer measurement times, the invention compensates by using the segmented HRTF approach that requires fewer measurement points, thus maintaining productivity while reducing device complexity and cost.
3Device complexity
If generalized HRTFs are used instead of individualized measurements, then device complexity is reduced, but measurement precision deteriorates due to mislocalization
Solution Approach 1:
The patent applies local quality by making the HRTF system partially individualized through the listener-specific vertical variations component. This selective personalization focuses computational and measurement resources on the specific aspects of HRTF that most affect localization accuracy, while using general components for the remainder.
Solution Approach 2:
The invention changes the parameter representation of HRTF by decomposing it into listener-specific and general components. This parameter transformation allows the system to achieve accurate localization with fewer parameters than a complete individualized HRTF would require.
4Measurement precision
If a priori information about HRTF is used to aid interpolation, then measurement precision is improved with fewer samples, but device complexity increases due to modeling requirements
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing the general lateral variations component that can be applied to multiple listeners. This preliminary processing reduces the computational burden during actual measurement and estimation phases, achieving improved precision without proportionally increasing overall system complexity.
Data Source
AI summary
A Head-Related Transfer Function. The Head-Related Transfer Function includes listener-specific and general components. The listener-specific component includes listener-specific, vertical variations in the Head-Related Transfer Function. The general component includes non-listener-specific, lateral variations in the Head-Related Transfer Function.