Automatic Speaker Calibration via Optical Pattern Detection
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Solution Overview
Problem
Traditional Hi-Fi audio systems with beamforming loudspeakers require manual calibration by technicians, making it difficult for users to accurately adjust the sweet spot without trial and error, as the application software lacks the capability to track the user's location, and changes in speaker placement necessitate reinstallation.
Innovation Solution
A system and method for calibrating a baseline parameter using a plurality of light sources on a slave speaker, detecting light points on a master speaker, creating a detected pattern, comparing it to a database of calibration patterns, determining the relative angle and distance, and calculating the baseline parameter to automatically configure the sound beam.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual calibration by technicians is used, then the sweet spot can be configured, but the process is time-consuming and requires professional intervention
Solution Approach 1:
The system enables automatic calibration where the audio device itself performs the measurement and configuration tasks previously requiring technician intervention. The processor automatically detects speaker positions, calculates baseline parameters, and configures the sweet spot without human input, making the system self-servicing.
Solution Approach 2:
The system performs preliminary automatic calibration measurements during initial setup to establish baseline parameters before normal operation. This preliminary action captures speaker positions and environmental characteristics, enabling subsequent automatic adjustments without requiring technician presence during regular use.
2Ease of operation
If graphical menu configuration is used, then users can adjust sweet spot location, but users cannot accurately verify their location without trial and error
Solution Approach 1:
The system implements feedback by having the processor detect the user's actual position using sensors or cameras, compare it with the selected sweet spot location, and provide visual or audible feedback indicating whether the user is correctly positioned. This closed-loop feedback enables precise location verification without trial and error.
Solution Approach 2:
The system replaces manual trial-and-error mechanical positioning with automated optical or sensor-based detection. The processor uses cameras or position sensors to automatically detect user location and provides precise digital measurement feedback, substituting the imprecise mechanical trial-and-error method with accurate automated measurement.
3Measurement precision
If technician measurement is used, then baseline parameter can be determined, but reinstallation is required when speaker location changes
Solution Approach 1:
The system transitions from static baseline parameters set during installation to dynamic parameters that can be automatically remeasured and updated. When speakers are relocated, the processor automatically performs new measurements and recalculates baseline parameters, making the system adaptable to changing configurations without requiring reinstallation.
Solution Approach 2:
The system performs preliminary automatic measurements of speaker positions and baseline parameters that can be quickly recalibrated when relocation occurs. This preliminary measurement capability is built into the system, enabling rapid adaptation to new speaker positions without requiring technician intervention or complete reinstallation procedures.
4Extent of automation
If automatic calibration using light sources and pattern recognition is implemented, then baseline parameter calculation becomes automated, but device complexity increases
Solution Approach 1:
The system introduces light sources as an intermediary medium to transmit positional information between speakers and the detecting device. Instead of complex direct measurement systems, simple light-emitting diodes serve as intermediaries that encode spatial information in visible patterns, enabling automated calibration through optical rather than electronic complexity.
Solution Approach 2:
The system uses visual copying of light patterns created by sequentially activated light sources on one speaker as detected by the other speaker. The detecting speaker captures images of these light patterns, and the processor analyzes the copied visual information to calculate baseline parameters, replacing complex direct measurement with simpler pattern recognition.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables users to accurately adjust the sweet spot without manual intervention, allowing for precise sound beam steering and reducing the need for repeated installations by automatically determining the relative angle and distance between speakers.
Implementation Method 1
detecting light points at a detection set on a master in the speaker pair
Data Source
AI summary
A system and method for calibrating a baseline parameter for a speaker pair, the calibrating includes activating a plurality of light sources one-by-one on a slave in the speaker pair, detecting light points at a detection set on a master in the speaker pair, creating a detected pattern of light points, comparing the detected pattern to a database of calibration patterns, determining a relative angle and distance between the master and the slave, calculating the baseline parameter using the determined distance and relative angle.