Nonlinear Post-Processor for Robust Beamforming
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional beamformers face a tradeoff between hardware complexity, performance, and robustness, particularly struggling to achieve robustness with a small number of microphones and often resulting in poor performance with large arrays due to narrow angular response peaks.
Innovation Solution
The implementation of a nonlinear post-processor system that selects desired and interference scenarios based on predefined descriptions to optimize signal processing, using a cascade of postfilters or adaptively selected postfilters to enhance beamforming performance and robustness with minimal hardware complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of microphones are used, then performance is improved, but device complexity increases and robustness becomes difficult to achieve
Solution Approach 1:
The patent changes the mathematical parameters of the beamforming system by using a nonlinear post-processor with scenario-based gain adjustment. Instead of relying on increasing the number of microphones to improve performance, the system uses adaptive gain parameters that select from predefined scenarios to achieve excellent performance with minimal microphones, thereby resolving the contradiction between performance and device complexity
Solution Approach 2:
The patent replaces the mechanical approach of using more microphones (physical hardware) with a computational approach using nonlinear signal processing. The post-processor uses scenario selection and gain adjustment algorithms to achieve the performance that would otherwise require additional microphones, substituting mechanical complexity with computational intelligence
2Device complexity
If conventional beamforming is used, then hardware size is reduced, but robustness deteriorates with increasing error in spatial configuration
Solution Approach 1:
The patent introduces dynamic adaptability through scenario-based gain adjustment. The system dynamically selects from multiple predefined scenarios based on the actual spatial configuration, allowing the beamforming system to adapt to errors in source location assumptions. This dynamic approach maintains robustness while keeping hardware size small, as the flexibility comes from software algorithms rather than hardware redundancy
Solution Approach 2:
The patent prepares multiple predefined scenarios in advance that cover various possible spatial configurations. By having these scenarios pre-computed and stored, the system can quickly select the appropriate one when processing audio, ensuring robust performance even when the actual configuration deviates from assumptions. This preliminary preparation enables robustness without requiring complex real-time computation or additional hardware
3Device complexity
If linear processing structure is used, then device complexity is minimized, but performance and robustness tradeoff deteriorates
Solution Approach 1:
The patent segments the processing into distinct functional stages: a linear beamforming front-end and a nonlinear post-processing stage. The post-processor independently handles scenario selection and gain adjustment, separating these functions from the linear processing. This segmentation allows the system to maintain simple linear hardware while achieving complex nonlinear performance through software-based scenario management
Solution Approach 2:
The patent adds a new dimension to the processing architecture by introducing scenario-based nonlinear post-processing. Instead of modifying the linear processing structure itself, the system adds a post-processing dimension that operates on the beamformer output. This additional dimension enables sophisticated scenario selection and gain adjustment without complicating the underlying linear hardware architecture
Data Source
AI summary
Provided are methods and systems for spatially selecting acoustic sources using a post-processor that consists of a selection of one postfilter from a set of postfilters, or a cascade of postfilters, where each postfilter is optimal for a particular scenario. Each postfilter individually is based on optimizing the gain for each time-frequency bin based on knowledge of (i) a spatial covariance matrix for the desired source, (ii) a spatial covariance matrix for the interfering sources, and (iii) microphone signals in some neighborhood of the current time-frequency bin.