Personalized HRTF Synthesis via Anthropometric Sparse Representation
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Solution Overview
Problem
The challenge lies in efficiently generating personalized head-related transfer functions (HRTFs) for human subjects due to anthropometric variability, which requires time-consuming measurements using specialized acoustic equipment, making it impractical for large-scale implementation.
Innovation Solution
A technique that generates personalized HRTFs based on a training dataset of anthropometric feature parameters and measured HRTFs, using sparse representation or ridge regression to derive a vector that represents the anthropometric features of a subject as a linear superposition of features from the training dataset, allowing for the synthesis of HRTFs without specialized equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized acoustic measuring equipment is used under anechoic conditions to measure personalized HRTFs, then measurement precision is improved, but loss of time increases and device complexity increases
Solution Approach 1:
The patent creates a computational model that copies the acoustic measurement process by using sparse representation to reconstruct HRTFs from a limited set of training measurements. Instead of performing complete physical measurements on each subject, the system creates a mathematical copy of the measurement process that can be completed rapidly using the derived sparse coefficients and training database.
Solution Approach 2:
The patent performs preliminary actions by pre-measuring HRTFs for a diverse set of training subjects under controlled anechoic conditions and storing these measurements in a database. This preliminary work allows subsequent personalization to be achieved rapidly through computational methods rather than requiring time-consuming new measurements for each subject.
2Measurement precision
If specialized acoustic measuring equipment is used under anechoic conditions to measure personalized HRTFs, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces the mechanical acoustic measurement system with a computational system. Instead of using complex physical measurement equipment for each subject, the invention uses signal processing algorithms, sparse representation mathematics, and database lookups to generate personalized HRTFs, substituting mechanical complexity with computational simplicity.
Solution Approach 2:
The system creates a computational model that copies the essential characteristics of the acoustic measurement process, allowing HRTF personalization to be achieved through mathematical reconstruction rather than physical measurement, thereby eliminating the need for complex specialized equipment at the point of use.
3Manufacturing precision
If complete HRTF measurements are performed for each human subject, then HRTF personalization accuracy is improved, but productivity decreases
Solution Approach 1:
The patent applies partial action by measuring only a limited subset of HRTF parameters for each new subject rather than performing complete measurements across all frequencies and spatial positions. The sparse representation technique identifies and measures only the most critical components, then reconstructs the complete HRTF set computationally, achieving sufficient personalization accuracy with reduced measurement effort.
Solution Approach 2:
The system uses computational copying to generate the complete personalized HRTF set from the partial measurements by referencing the pre-collected training database, allowing rapid personalization without requiring complete new measurements for each subject.
Data Source
AI summary
The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.