Single Microphone Noise Suppression Fallback System
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Solution Overview
Problem
Dual microphone noise suppression systems face challenges when a secondary microphone is blocked or fails, or when noise sources are in close proximity to the speech source, leading to misclassification and ineffective noise suppression.
Innovation Solution
A system that utilizes primary and secondary acoustic signals to generate both single and dual microphone noise estimates, with a noise estimate integrator combining these to determine a combined noise estimate, allowing for fallback to single microphone noise suppression when necessary, using inter-microphone level differences and energy analysis to discriminate between speech and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If dual microphone noise suppression is used, then noise suppression performance is improved, but system reliability deteriorates when secondary microphone is blocked or fails
Solution Approach 1:
The system dynamically switches between dual-microphone and single-microphone noise suppression modes based on the reliability of the secondary microphone. When the secondary microphone is detected to be blocked or failed, the system transitions from relying on inter-microphone level differences to using only the primary microphone with single-microphone noise suppression algorithms, thereby maintaining system reliability while adapting to changing conditions.
Solution Approach 2:
The system changes the operational parameters of noise suppression by switching between different algorithmic approaches. In dual-microphone mode, it uses inter-microphone level difference calculations; in single-microphone mode, it employs energy-based noise estimation and suppression. This parameter change allows the system to maintain effectiveness across different operational states.
2Object-affected harmful factors
If dual microphone spatial filtering is used, then noise from different directions is suppressed, but misclassification occurs when noise and speech are in same spatial location
Solution Approach 1:
The system dynamically selects between spatial filtering and energy-based noise suppression based on the spatial relationship between speech and noise sources. When speech and noise are detected in the same spatial location, the system transitions from spatial filtering to single-microphone energy-based suppression, preventing misclassification while maintaining noise suppression capability.
Solution Approach 2:
The system introduces an intermediate detection mechanism that monitors the spatial distribution of acoustic energy. When this intermediary detection indicates that speech and noise occupy the same spatial location, it triggers a switch from spatial filtering to alternative noise suppression methods, thereby preventing misclassification errors.
3Device complexity
If single microphone noise suppression is used, then system complexity is reduced, but noise suppression effectiveness deteriorates in adverse environments
Solution Approach 1:
The system achieves multi-functionality by integrating both dual-microphone and single-microphone noise suppression capabilities within a unified framework. The primary microphone serves dual purposes: as part of the dual-microphone array for spatial filtering when conditions permit, and as a standalone sensor for energy-based noise suppression when the secondary microphone is unavailable. This universality allows the system to maintain effectiveness across different operational modes without requiring separate dedicated systems.
Data Source
AI summary
Systems and methods for providing single microphone noise suppression fallback are provided. In exemplary embodiments, primary and secondary acoustic signals are received. A single microphone noise estimate may be generated based on the primary acoustic signal, while a dual microphone noise estimate may be generated based on the primary and secondary acoustic signals. A combined noise estimate based on the single and dual microphone noise estimates is then determined. Using the combined noise estimate, a gain mask may be generated and applied to the primary acoustic signal to generate a noise suppressed signal. Subsequently, the noise suppressed signal may be output.