Personalised HRTF Synthesis with Precomputed Timbre Components
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Solution Overview
Problem
Existing methods for HRTF synthesis and personalization in VR, AR, and gaming are complex and not well suited for runtime application, requiring significant computation and memory, and are prone to errors.
Innovation Solution
A method involving the storage and combination of partial HRTFs, each encoding specific user-perception properties, allowing for efficient synthesis of a full HRTF by combining pre-calculated variations, reducing computational and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional HRTF synthesis methods are used to achieve personalized spatial audio, then audio personalization quality is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The HRTF is divided into multiple independent components: head-related transfer function (HRTF), room impulse response (RIR), and audio signal. Each component is processed separately through dedicated processing stages, allowing parallel computation and reducing overall computational complexity while maintaining personalization quality.
Solution Approach 2:
User-specific parameters including anthropometric measurements, head-related transfer functions, and room impulse responses are pre-calculated and stored in databases before runtime. During actual audio rendering, only parameter selection and combination are required, dramatically reducing real-time computational load while preserving personalized audio quality.
2Measurement precision
If traditional HRTF synthesis methods are used to achieve accurate spatial audio localization, then audio localization accuracy is improved, but memory requirements increase
Solution Approach 1:
The system stores user-specific parameters (anthropometric data, HRTF parameters, RIR parameters) in a compact structured format with defined data types and constraints. This parameter-based storage approach uses significantly less memory than storing complete HRTF datasets, while maintaining the ability to generate accurate personalized spatial audio through parameter-driven synthesis.
3Adaptability or versatility
If traditional HRTF synthesis algorithms are used for runtime application, then audio personalization is achieved, but processing speed decreases
Solution Approach 1:
All computationally intensive HRTF calculations, including anthropometric measurements, head-related transfer function computations, and room impulse response simulations, are performed in advance and stored as pre-calculated parameters. At runtime, the system only performs parameter selection from databases and straightforward audio signal processing, enabling fast personalized audio rendering without sacrificing personalization accuracy.
Solution Approach 2:
The audio processing pipeline is segmented into distinct stages: parameter selection from pre-stored databases, HRTF application to audio signal, and room effect application. This segmentation allows each stage to be optimized independently and enables parallel processing where applicable, significantly improving runtime processing speed while maintaining personalization capabilities.
4Measurement precision
If multiple complete HRTFs are stored for different users to ensure audio quality, then audio personalization quality is improved, but storage space and validation complexity increase
Solution Approach 1:
Instead of storing complete HRTF datasets for each user, the system extracts and stores only the essential user-specific parameters that define individual acoustic characteristics. These extracted parameters (anthropometric measurements, HRTF parameters, RIR parameters) are sufficient to reconstruct personalized HRTFs at runtime, reducing storage requirements and validation complexity while preserving audio personalization quality.
Data Source
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AI summary
A computer-implemented method of synthesising an HRTF is disclosed. The method comprising: providing the HRTF of a subject measured at a particular measurement angle; processing the HRTF to remove localisation perception features of the HRTF, where the processing comprises: removing spectral notches from the measured HRTF, the resulting processed HRTF referred to as the HRTF'; and calculating a subject's HRTF timbre by subtracting a baseline HRTF at the measurement angle from the subject's HRTF', the baseline HRTF comprising a generalised response component such that the HRTF timbre comprises subject-specific variations in the HRTF. The method further comprises using the HRTF timbre to synthesise an HRTF. The method allows for generating a personalised timbre component of an HRTF to provide better personalisation of an HRTF, thereby providing improved binaural audio.