Earbud Microphone HRTF Estimation via Machine Learning
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Solution Overview
Problem
Existing audio systems require time-consuming and expensive personalized Head-Related-Transfer-Function (HRTF) measurements, leading to lower quality spatial audio when using generic HRTFs, and there is a need for improved spatial audio generation.
Innovation Solution
An audio system and method for estimating audio spatialization parameters using machine learning models trained on audio data from ear-worn microphones, allowing for accurate and precise HRTF estimation in normal use environments without specialized setups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personalized HRTF measurements are performed using traditional methods, then measurement precision is improved, but loss of time and device complexity increase
Solution Approach 1:
The patent replaces the traditional mechanical measurement system (microphones in ear canals, acoustic equipment, specialized lab setup) with a machine learning-based computational system. The ML model processes audio data from standard earbud microphones to estimate HRTFs, eliminating the need for complex physical measurement apparatus and significantly reducing measurement time while maintaining precision.
Solution Approach 2:
The patent creates a computational copy of the HRTF measurement process through machine learning. Instead of performing actual physical measurements with complex equipment, the system uses ML models trained on measurement data to predict HRTF parameters from readily available audio recordings, effectively copying the essential information without requiring the original measurement setup.
2Measurement precision
If personalized HRTF measurements are performed using traditional methods, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces the traditional mechanical measurement system (microphones in ear canals, acoustic equipment, specialized lab setup) with a machine learning-based computational system. The ML model processes audio data from standard earbud microphones to estimate HRTFs, eliminating the need for complex physical measurement apparatus and significantly reducing measurement time while maintaining precision.
Solution Approach 2:
The patent makes the HRTF measurement system universal by using standard earbud microphones that all users already possess, rather than requiring specialized measurement equipment. The same audio device used for music playback can also capture the necessary audio data for HRTF estimation, eliminating the need for separate measurement devices and reducing overall system complexity.
3Device complexity
If generic HRTFs are used instead of personalized measurements, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent creates a computational copy of the HRTF measurement process through machine learning. Instead of performing actual physical measurements with complex equipment, the system uses ML models trained on measurement data to predict HRTF parameters from readily available audio recordings, effectively copying the essential information without requiring the original measurement setup.
Solution Approach 2:
The patent enables the audio system to automatically perform HRTF estimation using the user's own audio data captured during normal usage. The system self-adjusts by processing the user's specific ear canal acoustics through the ML model, eliminating the need for manual measurement procedures while achieving personalized results that improve spatial audio quality.
4Reliability
If personalized HRTF measurements are performed, then spatial audio quality is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary action by training the machine learning model in advance using datasets from multiple users. This pre-trained model can then rapidly estimate HRTFs for new users without requiring time-consuming traditional measurements. The heavy computational work is done beforehand, enabling fast, on-the-fly HRTF estimation during normal device usage.
Solution Approach 2:
The patent replaces the traditional mechanical measurement system (microphones in ear canals, acoustic equipment, specialized lab setup) with a machine learning-based computational system. The ML model processes audio data from standard earbud microphones to estimate HRTFs, eliminating the need for complex physical measurement apparatus and significantly reducing measurement time while maintaining precision.
Data Source
AI summary
A method for estimation of one or more audio spatialization parameters for a specific user, a training method and an audio system is provided, wherein the estimation method comprises obtaining audio data comprising first second audio data by obtaining the first audio data from a first microphone arranged near, in, or at a first ear canal of the target user and the second audio data from a second microphone arranged near, in, or at a second ear canal of the target user; and providing the one or more audio spatialization parameters comprising: applying a model to the audio data for provision of a parameter estimate of the one or more audio spatialization parameters; determining the one or more audio spatialization parameters based on the parameter estimate; and outputting the one or more audio spatialization parameters.