Individualized HRTF Estimation Using Generative Neural Networks
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Solution Overview
Problem
Existing methods for determining individualized Head-Related Transfer Functions (HRTFs) are either time-consuming, require specialized equipment, or lead to errors due to the use of generic or another person's HRTFs, which is problematic for applications like augmented and virtual realities.
Innovation Solution
A method and system using a trained generative artificial neural network, specifically a conditional variational autoencoder, to determine individualized HRTFs from sparse measurement data collected using commercial off-the-shelf devices, such as a mobile phone with in-ear microphones, without the need for anthropometric information or specialized equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized devices and lengthy measurement processes are used, then measurement precision is improved, but measurement time and device complexity increase
Solution Approach 1:
The system performs preliminary training of a generative neural network model using HRTF data from multiple test subjects before actual use. This pre-computed model enables rapid individualized HRTF estimation without requiring lengthy measurement processes during actual deployment, resolving the contradiction between measurement precision and time consumption.
Solution Approach 2:
The system creates a generative model that copies and generalizes HRTF characteristics from multiple test subjects. This model can then generate individualized HRTFs for new users based on minimal measurement data, achieving high precision without requiring extensive measurements for each individual.
2Measurement precision
If specialized devices are used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system replaces expensive specialized measurement equipment with commercial off-the-shelf devices such as smartphones and standard microphones. The generative neural network model compensates for the lower quality of these simpler devices, enabling accurate HRTF estimation using accessible, low-cost equipment.
Solution Approach 2:
The system substitutes complex physical measurement systems with a computational approach using neural networks. Instead of relying on specialized acoustic measurement equipment, the system uses machine learning algorithms to process data from simple devices, achieving comparable or superior precision.
3Device complexity
If generic or another person's HRTF is used, then device complexity is reduced, but measurement precision and user experience deteriorate
Solution Approach 1:
The system transitions from using uniform generic HRTFs for all users to generating locally optimized individualized HRTFs for each user based on their specific anatomical characteristics and measurement data. This personalized approach maintains system simplicity while dramatically improving acoustic localization accuracy for each individual user.
Solution Approach 2:
The system moves from static generic HRTFs to dynamic individualized HRTFs that are generated on-demand for each user. The generative neural network adapts the HRTF parameters based on individual user characteristics, enabling the system to provide optimized performance for each user without increasing overall system complexity.
Data Source
AI summary
There is provided a system and method for determining individualized head related transfer functions (HRTF) for a user. The method including: receiving measurement data from the user, the measurement data generated by repeatedly emitting an audible reference sound at positions in space around the user and, during each emission, recording sounds received near each ear of the user, the measurement data including, for each emission, the recorded sounds and positional information of the emission; determining the individualized HRTF by updating a decoder of a trained generative artificial neural network model, the decoder receives the measurement data as input, the trained generative artificial neural network model including an encoder and the decoder, the generative artificial neural network model is trained using data gathered from a plurality of test subjects with known spectral representations and directions for associated HRTFs at different positions in space; and outputting the individualized HRTF.