Microphone Array Abnormal Channel Detection Neural Network
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Solution Overview
Problem
Existing microphone array systems lack an integrated model for detecting failure signals in individual microphones, leading to suboptimal performance in noisy or reverberant environments.
Innovation Solution
A method that involves receiving multi-channel sound source signals from a microphone array, synchronizing them based on spatial information, and using a neural network model to detect abnormal channels and generate compensation signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional microphone array systems are used without integrated detection models, then device complexity is reduced, but reliability of voice signal processing deteriorates in noisy environments
Solution Approach 1:
The patent merges the detection model and compensation model into an integrated neural network system that processes microphone array signals. The detection model identifies abnormal channels while the compensation model generates corrected signals, combining multiple functions into a unified architecture that improves reliability without proportionally increasing complexity.
Solution Approach 2:
The patent introduces intermediate processing stages including signal synchronization based on spatial information and probability threshold comparison. These intermediary steps bridge the raw microphone inputs and final voice output, enabling reliable abnormal channel detection and compensation while maintaining manageable system complexity through modular processing.
2Measurement precision
If neural network models are used for abnormal channel detection, then measurement precision of microphone performance improves, but device complexity increases
Solution Approach 1:
The patent segments the neural network functionality into distinct detection and compensation modules. The detection model specifically identifies abnormal channels by analyzing synchronized microphone signals, while the compensation model separately generates corrected signals. This segmentation enables high measurement precision for abnormal channel detection while managing complexity through functional separation.
Solution Approach 2:
The patent transforms the microphone array problem into a parameter-based detection task by computing probability values from synchronized signals and comparing them against thresholds. This parameter transformation approach enables precise measurement of channel abnormality while using standardized neural network operations that control computational complexity.
3Productivity
If signal synchronization based on spatial information is implemented, then productivity of voice processing improves, but device complexity increases
Solution Approach 1:
The patent performs signal synchronization based on spatial information as a preliminary step before abnormal channel detection and compensation. By pre-aligning the microphone signals using known spatial relationships, the system improves subsequent processing efficiency and accuracy while containing complexity in a dedicated preprocessing stage that enables faster overall voice processing.
Data Source
AI summary
Method and apparatus for detecting abnormal channel of microphone array detection and generating compensation signal are provided. A method includes receiving multi-channel sound source signals from a microphone array, synchronizing the multi-channel sound source signals based on spatial information of the microphone array, and detecting an abnormal channel of the microphone array by inputting the synchronized sound source signals and first conditional information to a neural network model configured to perform an inverse operation. The method further includes generating a compensation signal corresponding to an abnormal channel using a neural network model in response to an abnormal channel being detected.