Neural HRTF Retrieval for Personalized Spatial Audio

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Solution Overview

Problem

Existing spatialized audio technologies face challenges in efficiently personalizing head-related transfer functions (HRTFs) due to anatomical differences among listeners, and the resource-intensive nature of collecting and interpolating HRTFs for immersive audio experiences.

Innovation Solution

A neural field model combined with retrieval augmented generation (RAG) is used to personalize and spatially upsample HRTFs by retrieving similar HRTFs from a database and using subject-specific parameters for efficient fine-tuning, reducing the need for extensive measurements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If HRTFs are collected for each listener to achieve accurate personalization, then audio accuracy is improved, but collection time and resource consumption increase

Engineering Contradiction:

ImproveHRTF personalization accuracyVSAvoidCollection time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent uses neural fields to generate synthetic HRTF representations that copy the essential acoustic characteristics of real HRTFs without requiring actual measurements. The neural field model learns from a database of real HRTFs and creates accurate virtual representations that can be applied to any listener, eliminating the need for time-consuming physical measurements while maintaining personalization accuracy

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces the mechanical measurement process (physical HRTF collection) with a computational approach using neural fields. Instead of measuring actual HRTFs through hardware systems, the invention uses AI-based neural networks to simulate and generate HRTFs, substituting physical measurement mechanisms with digital computation to achieve faster and more efficient personalization

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If HRTFs are spatially interpolated from a small set of directions to any possible source direction, then spatial coverage is improved, but interpolation accuracy may deteriorate

Engineering Contradiction:

ImproveSpatial coverageVSAvoidInterpolation accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent changes the representation parameters from traditional spatial interpolation methods to neural field embeddings. Instead of interpolating between discrete directional measurements, the system uses neural networks to transform and map HRTF characteristics across the entire sphere, achieving smooth and accurate spatial coverage through parameter transformation rather than geometric interpolation

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent transitions from a 1D/2D directional sampling approach to a 3D spherical coverage using neural fields. By embedding HRTFs in a neural space and using spherical coordinate transformations, the system achieves comprehensive spatial coverage in a higher-dimensional representation space, allowing accurate interpolation across all possible source directions without the limitations of traditional directional sampling

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Device complexity

If traditional HRTF methods are used, then implementation simplicity is maintained, but computational resource consumption increases

Engineering Contradiction:

ImproveImplementation simplicityVSAvoidComputational resource consumption

Core Design Contradiction:

Device complexityVSUse of energy by moving object

Solution Approach 1:

The patent performs preliminary action by pre-training neural field models on large datasets of HRTFs during an offline phase. The complex computational work is done in advance to create efficient neural network representations that can be quickly applied to new listeners during real-time operation, separating the heavy computational burden from the execution phase and reducing real-time resource requirements

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the computational approach from traditional HRTF processing to neural field-based processing. By transforming HRTFs into neural network representations with optimized parameters, the system achieves more efficient computation during execution. The neural fields enable faster evaluation and reduced computational complexity compared to traditional methods, while maintaining implementation simplicity through a unified model architecture

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260067633A1Retrieval augmented neural field for generating spatial audio

Publication Date: 2026.03.05 MITSUBISHI ELECTRIC RESEARCH LABORATORIES INC

AI summary

Systems, methods, software, and devices are disclosed herein that transform anechoic audio signals into spatialized audio signals. An audio processing method includes identifying a target sound source direction and a reference head related transfer function (HRTF) associated with a target subject and obtaining one or more retrieved HRTFs from an HRTF dataset based at least on the reference HRTF and the target sound source direction. The method continues with executing a neural field model to produce an output based on an input. Example input includes the one or more retrieved HRTFs and the target sound source direction, and example output includes a predicted HRTF. The anechoic audio signal may then be processed based at least on the predicted HRTF to produce a spatialized audio signal.