Audio Device Self-Calibration for Microphone Blockage
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Solution Overview
Problem
Existing audio devices face challenges in noise suppression due to inconsistent performance characteristics among microphones, leading to suboptimal noise cancellation, especially when microphones are blocked, causing speech cancellation and distortion.
Innovation Solution
The system self-calibrates microphones by adapting a σ coefficient based on energy, magnitude, and phase thresholds in audio signal frames dominated by the desired audio source, maintaining a shadow coefficient to prevent speech cancellation and optimizing noise suppression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manufacturers provide calibration data for microphones, then noise suppression performance is improved for most microphones, but performance becomes suboptimal for microphones on the outer ends of manufacturing tolerances
Solution Approach 1:
The system performs self-calibration by automatically evaluating audio frames and adapting the σ coefficient without requiring manual intervention or manufacturer calibration data. The device monitors its own performance and adjusts parameters to optimize noise suppression for its specific microphone characteristics, making each device self-sufficient and eliminating the need for external calibration files.
Solution Approach 2:
The system dynamically changes the σ coefficient parameter based on real-time audio frame analysis. By adapting this parameter according to the specific microphone's performance characteristics and the audio signal properties, the system optimizes noise suppression performance for each individual microphone despite manufacturing variations.
2Reliability
If the system processes audio frames to identify desired audio sources, then noise suppression is enhanced, but processing complexity increases
Solution Approach 1:
The system applies partial action by only processing and analyzing audio frames that meet specific criteria (energy threshold, magnitude threshold, phase threshold). Rather than continuously processing all frames, it selectively processes only those likely dominated by the desired audio source, reducing computational complexity while maintaining effective noise suppression.
Solution Approach 2:
The system performs preliminary evaluation of audio frames against threshold criteria before initiating full noise suppression processing. This preliminary action filters out frames that don't meet the criteria, preventing unnecessary processing complexity and computational resources from being wasted on frames that won't benefit from enhanced noise suppression.
3Reliability
If the system adapts the σ coefficient for all frames, then noise suppression is optimized, but speech cancellation may occur when microphones are blocked
Solution Approach 1:
The system uses feedback from shadow coefficient monitoring to control σ coefficient adaptation. When the shadow coefficient falls below a threshold indicating microphone blockage, the system reduces or stops adaptation, preventing speech cancellation. This feedback mechanism allows the system to optimize noise suppression when conditions are good while protecting against harmful effects when microphones are blocked.
Solution Approach 2:
The system takes preliminary anti-action by monitoring the shadow coefficient in advance and reducing adaptation before speech cancellation can occur. When the shadow coefficient indicates potential blockage conditions, the system proactively reduces the adaptation rate or stops adaptation, preventing the harmful effect of speech cancellation before it fully develops.
Data Source
AI summary
An audio device performs self calibration with respect to an audio source location when processing an audio signal frame determined likely to be dominated by the audio source. One or more conditions for sub-bands within the audio frame are evaluated to help identify whether the frame is dominated by the audio source. If the conditions meet a threshold value for a number of sub-bands within the frame, the audio signal may be identified as one dominated by the desired audio source and an audio source location coefficient may be adapted. Additionally, when the audio source location coefficient falls below a threshold value, (e.g., suggesting that one of two or more microphones is blocked), noise suppression is reduced or eliminated for the frame or frame sub-bands to prevent suppression of a desired audio source component along with the noise component.