Speech Intelligibility Measurement in Multi-Speaker Audio Tuning
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Solution Overview
Problem
Tuning large-scale networked audio systems in complex environments, such as conference rooms or workplaces, is challenging due to the need for expert setup and configuration of microphones and speakers across multiple floors and areas, with existing test processes failing to accurately represent the system's performance.
Innovation Solution
An automated tuning method that identifies and tunes multiple speakers and microphones on a network, using test signals to detect operational components, establish background noise levels, and optimize parameters like gain and frequency response, enabling efficient setup and configuration without requiring expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single speaker signal is tested to identify feedback, then the test process is simple, but the system performance cannot be accurately represented when multiple speakers are used
Solution Approach 1:
The system segments the testing process by assigning different test signals to different speakers simultaneously. Each speaker receives a unique frequency-coded test signal, allowing individual speaker characteristics to be measured while all speakers operate together, thus representing the actual multi-speaker system performance accurately.
Solution Approach 2:
The system uses frequency-coded test signals that exceed the bandwidth of individual speakers. By testing with frequencies higher than any single speaker can reproduce, the system can identify feedback paths and speaker characteristics without requiring each speaker to handle the full frequency range, simplifying the test while maintaining accuracy.
2Manufacturing precision
If expert teams manually setup and test audio equipment, then configuration accuracy is high, but installation time and cost increase
Solution Approach 1:
The system performs self-diagnosis and self-configuration by automatically analyzing the frequency-coded test signals captured by microphones. The controller identifies speaker characteristics, feedback paths, and optimal tuning parameters without requiring expert manual intervention, enabling automated setup while maintaining high configuration accuracy.
Solution Approach 2:
The system uses microphone feedback to automatically tune speaker output parameters. By analyzing the frequency-coded test signals captured by microphones, the controller adjusts speaker settings to optimize performance, eliminate feedback, and achieve target sound pressure levels automatically.
3Measurement precision
If multiple speakers are tuned individually, then each speaker can be optimized, but the overall system configuration becomes complex
Solution Approach 1:
The system merges the testing and tuning process by having all speakers operate simultaneously with their respective frequency-coded test signals. This allows individual speaker optimization and system-wide configuration to occur in a single integrated process, reducing complexity while maintaining precision.
Solution Approach 2:
The system uses frequency coding as a parameter to differentiate test signals for each speaker. By assigning unique frequency ranges or codes to each speaker's test signal, the system can independently analyze and optimize each speaker's performance while managing the overall system configuration through a unified controller.
Data Source
AI summary
An example method of operation may include initiating an automated tuning procedure, detecting via one or more microphones a sound measurement associated with an output of one or more speakers at two or more locations, determining a number of speech transmission index (STI) values equal to a number of microphones, and averaging the speech transmission index values to identify a single speech transmission index value.