Dynamic Sweet Spot Calibration via Crowd-Density Mapping
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Solution Overview
Problem
Conventional audio systems often fail to provide optimal sound quality to a large portion of the audience as they are pre-calibrated for a default sweet spot, typically at the center of a venue, which may not align with the actual audience distribution, leading to poor sound experience for those seated outside this central area.
Innovation Solution
A computer-implemented method for dynamic sweet spot calibration, which involves receiving an image of the listening environment, generating a crowd-density map, and adjusting audio parameters to dynamically update the sweet spot location based on audience distribution, using deep learning-based techniques for image enhancement and crowd density estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the audio system is pre-calibrated for a default sweet spot at the center of the venue, then the sound quality is optimized for the central area, but the sound quality deteriorates for audience members seated outside this central area
Solution Approach 1:
The patent implements dynamic sweet spot calibration by continuously monitoring audience distribution through cameras and sensors, then real-time adjusting audio parameters to relocate the sweet spot to match the actual crowd density. This transforms the static, fixed sweet spot into a dynamic, adaptive one that moves with the audience, resolving the contradiction between optimized sound quality and adaptability to different audience distributions
Solution Approach 2:
The system changes audio parameters (such as speaker volume, phase, and timing) based on detected audience distribution patterns. By dynamically modifying these parameters in response to crowd density maps, the system maintains high sound quality across different audience configurations, effectively resolving the contradiction between precision calibration and adaptability
2Reliability
If the sweet spot is fixed at a central location, then the audio system provides best sound experience to a small portion of the audience, but this results in poor sound experience for the majority of audience members
Solution Approach 1:
The audio system performs self-calibration by automatically detecting audience distribution through integrated cameras and sensors, then autonomously adjusting its own audio parameters to optimize the sweet spot location. This self-service capability eliminates the need for manual recalibration and ensures the system continuously provides best sound experience to the largest possible audience portion
Solution Approach 2:
The system incorporates feedback loops where audience distribution data from cameras and sensors continuously informs audio parameter adjustments. This closed-loop control ensures the sweet spot location always corresponds to areas of high audience density, maximizing the number of satisfied audience members while maintaining high sound experience quality
3Adaptability or versatility
If manual calibration is performed to adjust sweet spot location, then the sound system can be optimized for specific audience distributions, but this process is time-consuming and requires professional intervention
Solution Approach 1:
The system automatically performs calibration by detecting audience distribution and independently adjusting audio parameters without requiring professional operators. This automated self-calibration process eliminates time-consuming manual procedures while maintaining flexible sweet spot location adjustment capability
Solution Approach 2:
The patent replaces manual mechanical calibration processes with automated optical and acoustic sensing systems. Cameras and sensors detect audience distribution, and computer algorithms automatically calculate and apply the necessary audio parameter adjustments, substituting time-consuming manual operations with rapid automated processing
Data Source
AI summary
A technique for dynamic sweet spot calibration. The technique includes receiving an image of a listening environment, which may have been captured under poor lighting conditions, and generating a crowd-density map based on the image. The technique further includes setting at least one audio parameter associated with an audio system based on the crowd-density map. At least one audio output signal may be generated based on the at least one audio parameter.