HRTF Dataset Generation via Interpolation and Combination
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Solution Overview
Problem
Current head-related transfer function (HRTF) datasets are often insufficient for accurate sound localization in immersive audio applications, as they may not cover a sufficient range of positions, radial distances, or user-specific variations, leading to compromised accuracy and practical limitations in generating correct interaural time and intensity differences.
Innovation Solution
The method involves interpolating within existing HRTF datasets and combining multiple HRTF datasets to generate a more comprehensive and user-specific HRTF dataset, using per-object minimum phase interpolation (POMP) and standardizing frequency responses to account for individual user characteristics and recording environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a discrete set of HRTFs is provided for specific positions, then the dataset size is manageable, but sound localization accuracy is compromised for positions without defined HRTFs
Solution Approach 1:
The patent creates virtual copies of measured HRTF data through interpolation algorithms. Instead of measuring every possible position, the system generates synthetic HRTFs for intermediate positions by copying and transforming data from nearby measured positions, achieving comprehensive coverage without proportional increase in measurement effort
Solution Approach 2:
The patent transforms HRTF data by changing parameters such as position coordinates, radial distance, and frequency characteristics. Through parameter interpolation and transformation, the system generates HRTFs for user-specific anatomical variations and positions not directly measured, resolving the contradiction between limited dataset size and comprehensive localization accuracy
2Measurement precision
If HRTFs are measured for each individual user, then sound localization accuracy is improved, but the time and cost of data collection increases significantly
Solution Approach 1:
The patent creates a universal HRTF framework that serves multiple users and purposes. A core dataset measured once can be universally applied and adapted through interpolation for different users and positions, making the measurement process multi-functional and reducing redundant data collection across different users
Solution Approach 2:
The patent performs preliminary HRTF measurements for a limited set of representative positions and users. These preliminary measurements serve as the foundation for generating comprehensive HRTF datasets through interpolation, eliminating the need for exhaustive measurements before deployment
3Ease of operation
If HRTF measurement environment is simplified, then ease of operation is improved, but measurement accuracy deteriorates due to distortions from objects and positioning issues
Solution Approach 1:
The patent introduces computational interpolation algorithms as intermediaries between the simplified measurement process and the final high-accuracy HRTF dataset. The interpolation process mediates by filling in gaps and correcting distortions algorithmically, allowing simplified physical measurement setups to produce accurate results
Solution Approach 2:
The patent creates virtual copies of ideal HRTF characteristics by interpolating from reference measurements. Even when physical measurements contain distortions, the system generates accurate HRTFs by copying and transforming data from less distorted reference positions and users
Data Source
AI summary
A system for generating a head-related transfer function, HRTF, dataset, the system comprising an HRTF dataset selection unit operable to select two or more HRTF datasets, a characteristic identification unit operable to identify characteristics of the selected HRTF datasets, an HRTF dataset modification unit operable to modify one or more elements of the one or more selected HRTF datasets in dependence upon deviations in identified characteristics of the HRTF datasets, and an HRTF dataset generation unit operable to generate a combined HRTF dataset comprising at least the modified HRTF elements.