Multi-way Analysis for 3D Audio Virtual Source Generation
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Solution Overview
Problem
Current methods for implementing multiple virtual sound sources in 3D audio processing are limited by the inability to combine processing blocks specific to each source's position, leading to inefficiencies in spatial audio signal processing.
Innovation Solution
The use of principal component analysis (PCA) and multi-way analysis techniques to decompose multi-dimensional transfer function databases into sets of basis functions and gain factors, allowing for the construction of filters that can emulate audio transfer characteristics for any direction in 3D space, enabling the creation of virtual sound sources with flexible directionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple copies of processing blocks (Hi, Hc, ITD) are used for each virtual source, then the number of virtual sources can be increased, but the system complexity and processing overhead increase proportionally
Solution Approach 1:
The patent applies universality by creating a single set of processing blocks (Hi, Hc, ITD) that can serve multiple virtual sources simultaneously through parameter adjustment. Instead of having dedicated processing blocks for each virtual source, the same blocks are reused with different parameter settings (azimuth, elevation, distance) to generate multiple virtual sources, thereby reducing system complexity while maintaining versatility.
Solution Approach 2:
The patent implements parameter changes by modifying the parameters (azimuth angle, elevation angle, distance) of the existing processing blocks to create different virtual sources. By changing these parameters, the same processing blocks can emulate audio transfer characteristics for different spatial positions, eliminating the need for separate processing blocks for each virtual source.
2Productivity
If PCA-based filtering method is used, then computational efficiency improves, but processing blocks specific to each source position cannot be combined
Solution Approach 1:
The patent applies dynamics by making the filtering method adaptive to different source positions. Instead of using a static PCA-based filter for all positions, the system dynamically adjusts the filter parameters (azimuth, elevation, distance) based on the specific virtual source position requirements, combining the computational efficiency of PCA with the flexibility of position-specific processing.
Solution Approach 2:
The patent segments the processing into two parts: a common PCA-based filtering stage that provides computational efficiency, and a position-specific parameter adjustment stage that provides adaptability. This segmentation allows the system to benefit from both the efficiency of unified processing and the flexibility of position-specific customization.
3Measurement precision
If conventional HRTF filtering with ITD is used, then accurate spatial positioning is achieved, but the processing structure cannot be shared across multiple virtual sources
Solution Approach 1:
The patent applies universality by designing a single processing structure (Hi, Hc, ITD blocks) that can accurately position multiple virtual sources through parameter adjustment. The same processing blocks are used for all virtual sources, with parameters like azimuth, elevation, and distance being modified to achieve different spatial positions, thereby maintaining accuracy while avoiding structural duplication.
Data Source
AI summary
It is disclosed to determine, for a direction being at least associated with a value of a first direction component and with a value of a second direction component, at least one weighting factor for each basis function of a set of basis functions, each of the basis functions being associated with an audio transfer characteristic, wherein said determining is at least based on a first set of gain factors, associated with the first direction component, and on a second set of gain factors, associated with the second direction component