Fast Binaural Renderer for Moving Audio Sources
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Solution Overview
Problem
Current binaural rendering technologies, such as the MPEG-H 3D audio standard, face limitations in spatial resolution due to fixed virtual loudspeaker configurations and high computational complexity, especially when handling moving audio sources in virtual reality applications, where the existing methods struggle to efficiently process the increased number of input signals and dynamic source positions.
Innovation Solution
The proposed fast binaural renderer directly converts object-based and channel-based signals to binaural outputs without intermediate virtual loudspeaker processing, groups sources hierarchically to reduce computational complexity, and splits BRIR filters into blocks and frames for frame-by-frame convolution based on source positions, allowing for efficient rendering of moving sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed virtual loudspeaker configurations are used for binaural rendering, then the rendering process is simple, but spatial resolution is limited
Solution Approach 1:
The patent applies dynamics by transitioning from fixed virtual loudspeaker configurations to dynamic source position handling. The system can process moving audio sources in real-time by updating source positions frame-by-frame, allowing the rendering system to adapt to changing spatial arrangements without requiring complex reconfiguration of the virtual loudspeaker layout itself.
Solution Approach 2:
The patent segments the binaural rendering process into distinct functional stages: source position determination, BRIR selection, and convolution processing. By dividing the rendering pipeline into these segments, the system can handle complex spatial resolution requirements through optimized processing at each stage rather than attempting to solve all complexity simultaneously.
2Measurement precision
If multiple input signals are processed for moving sources, then spatial accuracy improves, but computational complexity increases
Solution Approach 1:
The patent merges multiple input signals and their corresponding BRIR convolutions into a unified processing approach. By combining the processing of multiple sources through hierarchical clustering and shared BRIR lookup structures, the system achieves high spatial accuracy for multiple moving sources while reducing redundant computational operations.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing BRIRs for various source positions and environments before actual rendering occurs. This allows the system to quickly retrieve and apply appropriate BRIRs during real-time processing of moving sources, avoiding the need to compute all spatial transformations in real-time and thus reducing computational complexity.
3Reliability
If BRIR filters are applied to all sources, then rendering quality is maintained, but processing time increases
Solution Approach 1:
The patent applies local quality by selectively applying full BRIR convolution processing only to sources that require high-quality rendering based on their spatial characteristics and motion status. Sources that are stationary or have predictable motion patterns can use simplified processing, while only dynamically moving sources receive the full BRIR treatment, thus maintaining rendering quality where needed while reducing overall processing time.
Data Source
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AI summary
The present disclosure relates to the design of a fast binaural rendering for multiple moving audio sources. This disclosure takes the audio source signals which can be object-based, channel-based or a mixture of both, associated metadata, user head tracking data and binaural room impulse response (BRIR) database to generate the headphone playback signals. The present disclosure applies a frame-by-frame binaural rendering module which takes parameterized components of BRIRs for rendering moving sources. In addition, the present disclosure applies hierarchical source clustering and downmixing in the rendering process to reduce computational complexity.