Automatic Microphone Selection for Multi-Mic Audio Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional microphone networks with simultaneous microphone capture struggle to determine the best audio quality for audio conferencing, as signal-to-noise ratio analysis fails to account for subjective audio quality factors like distortion, frequency response, and spatial differences, leading to inconsistent and disruptive audio experiences.
Innovation Solution
A system that periodically samples audio signals from multiple microphones, uses a subjectively-trained neural network to assess audio quality, and automatically switches to the microphone with the best quality, ensuring seamless and high-quality audio transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If signal-to-noise ratio analysis is used to determine audio quality, then noise filtering is improved, but subjective audio quality factors like distortion and frequency response are not accounted for
Solution Approach 1:
The patent transforms the audio quality assessment from relying solely on objective signal-to-noise ratio parameters to incorporating multiple parameters including subjective quality factors. The system evaluates distortion, frequency response, spatial differences, and other characteristics alongside noise levels, fundamentally changing the assessment parameters to achieve comprehensive audio quality measurement that reflects both technical performance and perceptual quality.
2Adaptability or versatility
If multiple microphones are used simultaneously, then audio coverage is improved, but audio quality consistency deteriorates
Solution Approach 1:
The patent implements dynamic microphone selection that continuously monitors and evaluates audio quality from multiple microphones in real-time. Rather than statically using all microphones simultaneously, the system dynamically identifies and switches to the optimal microphone based on current audio conditions, maintaining consistent high-quality audio while preserving the adaptability benefits of having multiple microphones available.
Solution Approach 2:
The system employs feedback mechanisms where audio quality metrics from multiple microphones are continuously assessed and fed back into the selection process. This feedback loop enables the system to maintain audio quality consistency by automatically switching between microphones based on real-time performance evaluation, ensuring that only the best audio source is used at any given moment.
3Measurement precision
If automatic microphone switching is implemented, then audio quality is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service automation where the system autonomously performs audio quality assessment, microphone selection, and switching without requiring manual intervention. The automated quality evaluation system continuously monitors multiple microphones and automatically switches between them based on evaluated metrics, improving audio quality while managing system complexity through intelligent automation rather than manual control mechanisms.
Data Source
AI summary
A computer-implemented method of audio processing comprises receiving, by at least one processor, multiple audio signals from multiple microphones. The audio signals are associated with audio emitted from a same source. The method also may include determining an audio quality indicator of individual ones of the audio signals using a neural network, and selecting at least one of the audio signals depending on the audio quality indicators.