Real-time Edge Diffraction Audio Simulation via ML Filters
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Solution Overview
Problem
Conventional diffraction modeling in computer-generated reality (CGR) environments requires significant computational power and cannot simulate edge-diffraction in real-time, leading to discontinuities in three-dimensional sound field rendering and impacting user immersion.
Innovation Solution
A computer system that uses machine-learning algorithms to determine edge-diffraction filter parameters based on listener, source, and object geometry, applying these filters to audio signals to simulate edge-diffraction in real-time, enabling accurate spatial audio localization cues without complex numerical computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diffraction modeling is used in CGR environments, then accurate sound field rendering can be achieved, but significant computational power is required and real-time simulation is not possible
Solution Approach 1:
The patent creates a computational model that copies the essential acoustic behavior of edge diffraction using simplified geometric representations and pre-computed filter parameters. Instead of performing complex numerical computations in real-time, the system uses machine-learning-derived filters that replicate diffraction effects, enabling real-time audio rendering while maintaining accuracy.
Solution Approach 2:
The patent transforms the complex physical simulation problem into a parameter-based approach by using machine-learning algorithms to pre-compute filter parameters (such as cutoff frequencies and gain values) based on geometric characteristics. These parameters are then applied through standard audio processing filters, changing the computational approach from real-time physical simulation to real-time parameter application.
2Reliability
If conventional diffraction modeling is used, then accurate edge-diffraction simulation is achieved, but the system cannot operate in real-time
Solution Approach 1:
The patent performs the computationally intensive machine-learning-based filter parameter computation in advance, before real-time audio rendering is needed. The system pre-computes the appropriate filters based on the geometric characteristics of virtual objects and their positions, so that during real-time operation, only simple filter application is required, eliminating time loss.
Solution Approach 2:
The patent replaces complex numerical computation mechanisms with simpler audio signal processing mechanisms. Instead of performing time-consuming physical field simulations, the system substitutes these with machine-learning-derived filter applications that achieve the same acoustic effects through more efficient signal processing operations.
3Measurement precision
If complex numerical computations are used for diffraction modeling, then accurate spatial audio localization is achieved, but computational complexity increases significantly
Solution Approach 1:
The patent creates simplified copies of the complex diffraction physics through pre-computed filter parameters that capture the essential acoustic behavior. These filters replicate the spatial localization effects of complex numerical computations without requiring the same level of computational complexity during real-time operation.
Solution Approach 2:
The patent introduces machine-learning-derived filter parameters as an intermediary between the complex geometric configuration and the final audio output. These intermediaries (filter parameters) translate complex spatial relationships into straightforward filter applications, reducing computational complexity while maintaining localization accuracy.
Data Source
AI summary
A computer system having an electronic device determines a listener position within a computer-generated reality (CGR) setting that is to be aurally experienced by a user of the electronic device through at least one speaker. The system determines a source position of a virtual sound source within the CGR setting and determines a characteristic of a virtual object within the CGR setting, where the characteristic include a geometry of an edge of the virtual object. The system determines at least one edge-diffraction filter parameter for an edge-diffraction filter based on 1) the listener position, 2) the source position, and 3) the geometry. The system applies the edge-diffraction filter to an input audio signal to produce a filtered audio signal that accounts for edge-diffraction of sound produced by the virtual sound source within the CGR setting.