Directional Audio Capture Using Phase Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing directional audio capture systems are non-uniform in suppression across the capture area, sensitive to microphone sealing, and compromised by varying distances between the talker and the audio device, leading to inconsistent noise suppression.
Innovation Solution
The method correlates phase plots of audio inputs to estimate salience at different directional angles, providing cues for attenuation or amplification, and adjusts operational modes based on salience peaks to enhance directional audio capture, using attack and release time constants to control suppression levels and avoid artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If existing directional audio capture systems are used to suppress sounds outside the lobe, then noise suppression is achieved, but the suppression becomes non-uniform when talker distance varies
Solution Approach 1:
The system dynamically adjusts the beamforming weights and suppression characteristics based on the detected talker distance. As the talker moves closer or farther, the system adapts the directional pattern to maintain uniform suppression across the lobe, transforming a static system into a dynamic one that responds to environmental changes.
Solution Approach 2:
The system uses feedback from the audio signals to detect talker position and distance, then adjusts the beamforming parameters accordingly. This closed-loop control ensures that the suppression remains uniform despite variations in talker distance, resolving the contradiction between noise suppression and robustness.
2Manufacturing precision
If microphone sealing is improved to achieve more uniform suppression, then suppression uniformity increases, but device consistency becomes poor due to manufacturing variations
Solution Approach 1:
The system performs self-calibration by automatically detecting the actual microphone characteristics and sealing quality during operation. It adjusts the beamforming weights to compensate for manufacturing variations, allowing each device to self-correct its specific imperfections and achieve consistent performance across the production batch.
Solution Approach 2:
The system changes the operational parameters (beamforming weights, gain factors) based on the detected microphone sealing quality. By dynamically adjusting these parameters, the system compensates for manufacturing variations and maintains uniform suppression across all devices, regardless of sealing quality differences.
3Manufacturing precision
If calibration is performed to meet customer requirements, then suppression characteristics are optimized, but off-the-box calibration may disagree with actual customer needs
Solution Approach 1:
The system performs preliminary calibration during manufacturing with default settings, but also prepares for post-deployment adaptation. The initial calibration provides a baseline, while the system retains the capability to further optimize suppression characteristics based on actual customer usage patterns and environmental conditions.
Solution Approach 2:
The system transitions from static off-the-box calibration to dynamic adaptation, where suppression characteristics can be adjusted based on actual customer requirements and environmental feedback. This allows the system to evolve from a fixed configuration to an adaptive one that meets real-world needs.
4Reliability
If uniform suppression is enforced across all angles, then noise suppression consistency improves, but sensitivity to microphone sealing increases
Solution Approach 1:
The system uses feedback to detect the actual microphone sealing quality and adjusts the beamforming weights to achieve uniform suppression without being overly sensitive to sealing variations. By measuring the actual performance and compensating for sealing differences, the system maintains consistency while reducing sensitivity to manufacturing variations.
Data Source
AI summary
Systems and methods for improving performance of a directional audio capture system are provided. An example method includes correlating phase plots of at least two audio inputs, with the audio inputs being captured by at least two microphones. The method can further include generating, based on the correlation, estimates of salience at different directional angles to localize a direction of a source of sound. The method can allow providing cues to the directional audio capture system based on the estimates. The cues include attenuation levels. A rate of change of the levels of attenuation is controlled by attack and release time constants to avoid sound artifacts. The method also includes determining a mode based on an absence or presence of one or more peaks in the estimates of salience. The method also provides for configuring the directional audio capture system based on the determined mode.