Spatialized Soundfield Beamforming for Multi-Listener Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing spatialized sound systems struggle to accurately deliver 3D audio to multiple listeners in non-stationary positions, particularly outside the sweet spot, due to issues with cross-talk cancellation and varying head-related transfer functions (HRTFs) between individuals, leading to distorted sound localization and limited sweet spots.
Innovation Solution
A method for signal processing that optimizes sound waveforms from a sparse speaker array using adaptive beamforming and head-related transfer functions (HRTFs) to deliver spatialized sound, allowing for accurate sound localization and control over the sound field for multiple listeners, even in non-stationary positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-talk cancellation is used to deliver spatialized sound to multiple listeners, then sound localization accuracy is improved for listeners in the sweet spot, but sound localization becomes distorted for listeners outside the sweet spot
Solution Approach 1:
The system dynamically adapts the audio signal processing based on the listener's position. Head tracking sensors detect listener movement and the system adjusts the spatialized sound delivery in real-time, transitioning from static sweet spot optimization to dynamic multi-position adaptability. This resolves the contradiction by making the system flexible rather than fixed.
Solution Approach 2:
The system changes audio parameters including HRTF selection, beamforming coefficients, and spatialization filters based on detected listener position. By adjusting these parameters dynamically, the system maintains accurate sound localization for multiple listeners at different positions rather than being limited to a fixed sweet spot configuration.
2Measurement precision
If head-related transfer functions (HRTFs) are optimized for a single listener, then sound localization accuracy is improved for that listener, but the system becomes ineffective for multiple listeners with different HRTFs
Solution Approach 1:
The system achieves multi-listener compatibility by implementing universal adaptability through head tracking and dynamic HRTF selection. A single system serves multiple listeners with different HRTFs by automatically adapting to each listener's position and characteristics, making the system universally applicable rather than listener-specific.
Solution Approach 2:
The system dynamically switches between different HRTF sets based on the detected listener's position and characteristics. This dynamic adaptation allows the system to optimize sound localization for each individual listener in real-time, resolving the contradiction between single-listener optimization and multi-listener effectiveness.
3Device complexity
If a sparse speaker array is used to reduce system complexity, then device complexity is reduced, but the ability to deliver accurate spatialized sound to multiple listeners deteriorates
Solution Approach 1:
The system compensates for the limitations of a sparse speaker array by dynamically adjusting audio parameters including beamforming coefficients, spatialization filters, and HRTF selections. These parameter changes enable accurate spatialized sound delivery despite the reduced number of speakers, resolving the contradiction between system simplicity and audio accuracy.
Solution Approach 2:
The system uses virtual speaker images and simulated acoustic paths to create the perception of a more complex speaker array. Through digital signal processing and HRTF-based spatialization, the sparse physical array creates virtual acoustic environments that mimic what would be produced by a denser speaker configuration.
4Area of stationary object
If the sweet spot is enlarged to accommodate multiple listeners, then listener coverage area is increased, but sound localization accuracy for individual listeners deteriorates
Solution Approach 1:
The system replaces the static sweet spot concept with dynamic listener tracking. Instead of enlarging the sweet spot area, the system actively tracks each listener's position and adjusts the spatialized sound delivery in real-time. This maintains high sound localization accuracy for each individual listener regardless of their position within a larger coverage area.
Solution Approach 2:
The system segments the audio delivery for each listener individually based on their detected position. Rather than providing a single optimized signal for a large sweet spot area, the system creates individualized audio paths for each listener, maintaining localization accuracy while accommodating multiple listeners across a larger spatial region.
Data Source
AI summary
A signal processing system and method for delivering spatialized sound by optimizing sound waveforms from a sparse array of speakers to the ears of a user. The system can provide listening areas within a room or space, to provide spatialization sounds to create a 3D audio effect. In a binaural mode, a binary speaker array provides targeted beams aimed towards a user's ears.