Audio Signal Processor for Low-Latency Binaural Synthesis
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Solution Overview
Problem
Existing binaural audio systems face challenges with high computational complexity, require complex room geometric data, and suffer from motion-to-sound latency, making them unsuitable for mobile and wearable devices, and often result in non-natural sound perception and reduced externalization.
Innovation Solution
An audio signal processor that synthesizes two-channel acoustic data from single-channel data, integrating directivity information, enhancing early reflections, calculating late reverberation using binaural noise, and distributing processing tasks across devices to reduce computational load and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex binaural synthesis algorithms are used to achieve high-quality externalization, then sound localization precision is improved, but computational complexity increases making them unsuitable for mobile devices
Solution Approach 1:
The patent segments the binaural synthesis process into distinct components: HRTF processing for directional sound, RIR processing for room acoustics, and their convolution combination. This segmentation allows each component to be optimized independently and processed at different rates, reducing overall computational burden while maintaining localization precision.
Solution Approach 2:
The patent pre-computes and stores HRTF and RIR data in lookup tables before runtime processing. During actual audio rendering, the system only needs to retrieve pre-computed values and perform simple convolutions, dramatically reducing real-time computational complexity while preserving high-quality sound externalization.
2Ease of operation
If real-time binaural rendering is performed on mobile devices with limited processing power, then device portability is improved, but motion-to-sound latency increases beyond acceptable thresholds
Solution Approach 1:
The system pre-computes HRTF and RIR lookup tables offline, so that during mobile device operation, only lightweight data retrieval and convolution operations are performed in real-time. This preliminary preparation eliminates the need for complex real-time calculations, reducing motion-to-sound latency to below 50ms while maintaining device portability.
Solution Approach 2:
The patent uses pre-computed copies of HRTF and RIR data stored in lookup tables during runtime processing. Instead of calculating these complex acoustic models in real-time on mobile devices, the system retrieves pre-computed copies and applies them through simple convolution, dramatically reducing processing time and latency.
3Adaptability or versatility
If wireless transmission is used to connect mobile devices with audio processing units, then device flexibility is improved, but additional transmission delay is introduced exceeding maximum latency requirements
Solution Approach 1:
The patent extracts the computationally intensive binaural synthesis calculations from the wireless-connected processing unit and performs them locally on the mobile device using pre-computed lookup tables. This extraction of heavy computation to the mobile device eliminates the need for continuous complex data transmission over wireless channels, reducing latency while maintaining flexibility.
4Measurement precision
If accurate room acoustic simulation is performed using complex geometric models, then sound naturalness is improved, but processing requirements increase making them unsuitable for wearable devices
Solution Approach 1:
The patent pre-computes room acoustic models (RIR) for various virtual environments and stores them in lookup tables before runtime. During actual use on wearable devices, the system only needs to retrieve appropriate pre-computed RIR data and perform simple convolutions with the audio signal, eliminating the need for complex real-time geometric modeling while maintaining natural sound reproduction.
Solution Approach 2:
The system uses pre-computed copies of accurate room acoustic models stored in lookup tables during runtime processing on wearable devices. These pre-computed RIR data capture complex acoustic effects including reflections, diffraction, and shadowing, allowing high-fidelity sound naturalness without requiring complex real-time geometric calculations.
Data Source
AI summary
Audio signal processor for generating a two-channel audio signal, comprising: an input interface for providing single-channel acoustic data describing an acoustic environment; a two-channel synthesizer for synthesizing two-channel acoustic data from the single-channel acoustic data using a listener position or rotation; and a sound generator for generating the two-channel audio signal from an audio signal and the two-channel acoustic data, wherein the input interface is configured to acquire a raw representation related to the single-channel acoustic data, and to derive the single-channel acoustic data using the raw representation and additional data stored in the audio signal processor or accessible by the audio signal processor.