Auto-calibrating Speaker Tracking Systems for Conference Rooms
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Solution Overview
Problem
Existing speaker tracking systems in video conference endpoints often require manual calibration and struggle to accurately frame active speakers when multiple systems are deployed at different angles, leading to suboptimal far-end experiences due to the lack of automatic spatial calibration.
Innovation Solution
The system automatically calibrates multiple speaker tracking systems by collecting data points from active speakers using cameras and microphone arrays, determining a reference coordinate system, and calculating the spatial locations of secondary systems relative to a master system, allowing for dynamic repositioning and improved framing without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple speaker tracking systems are deployed at different angles to improve framing, then the far-end experience is improved, but manual calibration complexity increases
Solution Approach 1:
The speaker tracking systems automatically calibrate themselves by detecting common active speakers and computing relative positions without manual intervention. The system uses data from multiple tracking systems to determine a reference coordinate system and calculate spatial relationships autonomously, eliminating the need for manual calibration while maintaining accurate framing across multiple angled deployments.
2Measurement precision
If multiple speaker tracking systems share and combine data to improve active speaker detection, then framing quality improves, but system complexity increases
Solution Approach 1:
The system merges data from multiple speaker tracking systems by collecting data points from each system and combining them to determine a unified reference coordinate system. This integration allows the systems to share information about active speakers and compute relative positions, improving detection accuracy while managing complexity through systematic data fusion rather than ad-hoc processing.
3Measurement precision
If manual calibration is performed to ensure accurate spatial positioning, then measurement precision is maintained, but time consumption increases
Solution Approach 1:
The system performs preliminary automatic calibration by detecting common active speakers and computing relative positions before actual video conferencing begins. This preliminary action establishes the reference coordinate system and spatial relationships in advance, eliminating the need for time-consuming manual calibration while ensuring measurement precision is maintained from the start of operations.
Data Source
AI summary
A system that automatically calibrates multiple speaker tracking systems with respect to one another based on detection of an active speaker at a collaboration endpoint is presented herein. The system collects a first data point set of an active speaker at the collaboration endpoint using at least a first camera and a first microphone array. The system then receives a plurality of second data point sets from one or more secondary speaker tracking systems located at the collaboration endpoint. Once enough data points have been collected, a reference coordinate system is determined using the first data point set and the one or more second data point sets. Finally, after a reference coordinate system has been determined, the system generates the locations of the one or more secondary speaker tracking systems with respect to the first speaker tracking system.