Personalized HRTF Tuning Through Virtual Sound Position Feedback
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Solution Overview
Problem
Existing HRTF personalization methods require cumbersome user input of measurements and are not adjustable once generated, limiting personalization accuracy and user satisfaction.
Innovation Solution
A method for updating a base HRTF model by receiving user indications on the perceived position of a virtual sound source, adjusting parameters to generate a personalized HRTF model that accurately matches the user's experience, allowing real-time adjustment and fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a base HRTF model with generic parameters is used, then device complexity is reduced and ease of operation is improved, but HRTF personalization accuracy deteriorates
Solution Approach 1:
The system changes the parameters of the base HRTF model based on user feedback about perceived sound source positions. By adjusting parameters like interaural time differences, interaural level differences, and spectral notches according to user indications, the system transforms a generic model into a personalized one without requiring manual measurement input from the user.
Solution Approach 2:
The system implements a feedback loop where user indications about the perceived position of virtual sound sources are received and used to update the HRTF model parameters. This iterative feedback process allows the system to learn and adapt to the specific user's auditory characteristics, improving personalization accuracy while maintaining ease of operation.
2Ease of operation
If pre-set HRTFs with limited options are provided, then device complexity is reduced and ease of operation is improved, but adaptability deteriorates
Solution Approach 1:
The system transitions from static pre-set HRTFs to a dynamic model that adapts based on user feedback. The HRTF parameters are not fixed but are continuously adjusted according to user indications about perceived sound positions, enabling the system to adapt to individual user characteristics while maintaining a simple initial configuration.
Solution Approach 2:
Instead of providing multiple fixed HRTF presets, the system starts with a base model and modifies its parameters based on user feedback. This approach provides unlimited adaptability within the model framework while maintaining ease of operation, as users only need to provide simple position feedback rather than selecting from limited presets.
3Measurement precision
If user measurements are required for HRTF generation, then HRTF personalization accuracy is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system enables users to participate in their own HRTF personalization through simple feedback about perceived sound positions. Instead of requiring users to perform complex measurements on themselves, the system uses their natural auditory perception and simple indications to automatically adjust the model, making the process both accurate and easy to operate.
Solution Approach 2:
The system replaces the mechanical measurement process (physical measurement of head and ear dimensions) with an acoustic-based approach using user feedback on perceived sound positions. This substitution eliminates the need for physical measurement tools and procedures while achieving comparable or superior personalization accuracy through psychoacoustic principles.
Data Source
AI summary
An audio personalisation method for generating a personalised Head-Related Transfer Function, HRTF, the method comprising the steps of: obtaining a base HRTF model comprising a first set of parameters; outputting a sound from a virtual sound source according to the base HRTF model, the virtual sound source having an intended virtual position; receiving at least one user indication corresponding to a user virtual position of the virtual sound source, wherein the intended virtual position is different to the user virtual position; updating the first set of parameters to a second set of parameters based on the at least one indication from the user so as to update the user virtual location; and generating a personalised HRTF model comprising the second set of parameters, wherein the difference between the intended virtual position and the user virtual position according to the personalised HRTF model is less than the difference between the intended virtual position and the user virtual position according to the base HRTF model.