Adaptive Noise Cancellation Divergence Detection via Frequency Weighting
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Solution Overview
Problem
Adaptive noise-cancellation systems face challenges in detecting divergence, which can lead to increased noise levels instead of noise reduction, as existing divergence detection methods are inadequate in monitoring the convergence condition of adaptive filters.
Innovation Solution
A system and method for detecting divergence in noise-cancellation systems that normalize the power of error signal components, use a smoothed time gradient, and weight frequencies to determine if the system is diverging by comparing time-averaged values to thresholds, allowing for corrective actions to mitigate divergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing divergence detection methods are used, then the system structure remains simple, but the divergence detection precision is insufficient leading to inadequate monitoring of convergence condition
Solution Approach 1:
The error signal is decomposed into multiple frequency components, and the power spectrum is divided into different frequency bands. Each frequency component is analyzed separately to detect divergence, improving detection precision by examining specific frequency regions where divergence may occur.
Solution Approach 2:
The detection method transitions from analyzing the overall error signal power to examining the power spectral density across multiple frequencies. This dimensional expansion from time domain to frequency domain enables more precise detection of divergence patterns that may be obscured in the aggregate signal.
2Measurement precision
If the system continuously monitors convergence condition with high precision, then the divergence detection accuracy improves, but the computational energy consumption increases
Solution Approach 1:
Instead of uniformly monitoring all frequency components with equal precision, the method focuses computational resources on specific frequency bands where divergence is most likely to occur. The power spectral density analysis concentrates on critical frequency regions, reducing overall computational load while maintaining detection accuracy.
Solution Approach 2:
The system performs partial monitoring by selecting specific frequency components for detailed analysis rather than processing the entire spectrum continuously. This selective approach reduces computational energy requirements while maintaining sufficient detection precision for critical divergence scenarios.
3Reliability
If the system responds quickly to detected divergence, then the noise amplification is prevented more effectively, but the false divergence detection may trigger unnecessary corrective actions
Solution Approach 1:
The system continuously monitors the power spectral density of the error signal and compares it against reference levels. When divergence is detected in specific frequency bands, corrective actions are triggered. The continuous feedback loop allows the system to distinguish between transient fluctuations and genuine divergence patterns, reducing false detections while maintaining rapid response to actual divergence events.
Data Source
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AI summary
A system for detecting divergence in a noise-cancellation system, comprising: a controller configured to: determine a power of a component of the error signal, the component being correlated to the at least one reference sensor signal; determine an average value, over a first time period, of a value representative of a time gradient of the power of the component of the error signal; and determine whether the average value is greater than a threshold.