Audio Profile Clustering for Faster Spatial Audio Calibration
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Solution Overview
Problem
Existing audio output devices struggle with accurately generating spatial audio due to variations in ear shapes among users, leading to inconsistent perception of sound locations, which can result in ineffective calibration processes that are time-consuming or tiresome for users.
Innovation Solution
A computer-implemented method involving generating vector representations of candidate audio profiles, clustering them, and presenting audio test patterns to users for feedback to determine an effective audio profile, reducing the search space and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a calibration process is performed to model the user's audio profile by presenting multiple sounds and asking the user to indicate perceived locations, then the spatial audio accuracy is improved, but the calibration process becomes lengthy and time-consuming for the user
Solution Approach 1:
The system performs preliminary actions by pre-generating a large set of candidate audio profiles through virtual simulations of sound propagation around human head geometries before the calibration process. This pre-computation reduces the real-time calibration burden by providing a ready pool of candidate profiles for selection
Solution Approach 2:
The system creates virtual copies of human head geometries and simulates sound propagation around these digital models to generate candidate audio profiles. These virtual copies allow the system to pre-compute multiple potential audio profiles without requiring physical measurements from each user
2Measurement precision
If the audio output device presents multiple audio profiles for user selection, then the spatial audio accuracy for individual users is improved, but the process becomes time-consuming or tiresome for the user
Solution Approach 1:
The system introduces an intermediary automated selection process that uses machine learning models to predict the most suitable audio profile from candidate profiles based on user characteristics. This intermediary reduces the burden on users by automatically narrowing down options rather than requiring manual evaluation of multiple profiles
Solution Approach 2:
The system replaces the mechanical interaction of manual audio profile selection with an automated machine learning-based selection process. The ML model substitutes for the user's manual evaluation work by automatically determining the optimal audio profile match
3Measurement precision
If the calibration process uses a large search space of possible audio profiles and parameters, then the accuracy of spatial audio is improved, but the calibration process becomes excessively long and may not complete within reasonable time
Solution Approach 1:
The system segments the large search space of audio profiles into multiple smaller clusters or groups. This segmentation allows the calibration process to work with manageable subsets of candidates rather than the entire large space, improving processing efficiency while maintaining accuracy
Solution Approach 2:
The system performs partial action by selecting and evaluating only the most promising candidate audio profiles from the full set, rather than exhaustively testing all possible profiles. This selective approach achieves sufficient accuracy without the computational burden of complete evaluation
Data Source
AI summary
Techniques for selecting an audio profile for an audio output device include clustering a plurality of candidate audio profiles into a plurality of clusters based on vector representations of each of the plurality of candidate audio profiles, selecting a first candidate audio profile that is representative of a first cluster of the plurality of clusters, presenting, to a user, an audio test pattern rendered based on the first candidate audio profile, and determining an audio profile for an audio output device based on a response of the user to the presented audio test pattern.