Blind Source Separation for Cellular Interference Suppression
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Solution Overview
Problem
Existing cellular telephone interference suppression techniques using single microphones provide limited quality improvement, especially in noisy environments with low signal-to-noise ratios, and introduce musical noise artifacts, while blind source separation methods are challenging due to size and computational resource constraints in mobile devices.
Innovation Solution
Implementing blind source separation using two or more microphones to separate desired speech content from interference, with reduced computational complexity through the approximation of tangent hyperbolic functions using lookup tables and interpolation, and additional post-processing to estimate and remove remaining interference, suitable for real-time execution in cellular telephones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If blind source separation is implemented using two or more microphones, then speech quality is improved, but device complexity increases
Solution Approach 1:
The blind source separation process is divided into distinct stages: initial separation using BSS algorithms, followed by post-processing to remove residual interference. This segmentation allows each stage to focus on specific aspects of interference suppression, improving overall speech quality while managing computational complexity through modular processing
Solution Approach 2:
The patent applies post-processing techniques to address remaining interference after the primary BSS separation. This partial action approach handles only the residual interference rather than attempting complete separation in one step, reducing the computational burden while maintaining high speech quality
2Object-affected harmful factors
If blind source separation is implemented with full computational processing, then interference suppression is improved, but computational resources are exceeded
Solution Approach 1:
The patent implements a two-stage approach where post-processing handles only the residual interference after initial BSS separation, rather than applying full computational processing to the entire separation task. This reduces overall computational resource consumption while maintaining effective interference suppression
Solution Approach 2:
The post-processing stage continuously refines the separated speech signal by removing residual interference, building upon the initial BSS separation results. This continuous refinement approach achieves high interference suppression with reduced computational effort compared to attempting complete separation in a single computational pass
Data Source
AI summary
A method of interference suppression is provided that includes receiving a first audio signal from a first audio capture device and a second audio signal from a second audio capture device wherein the first audio signal includes a first combination of desired audio content and interference and the second audio signal includes a second combination of the desired audio content and the interference, performing blind source separation using the first audio signal and the second audio signal to generate an output interference signal and an output audio signal including the desired audio content with the interference suppressed, estimating interference remaining in the output audio signal using the output interference signal, and subtracting the estimated interference from the output audio signal to generate a final output audio signal with the interference further suppressed.