Sinusoidal Active Noise Reduction Instability Detection

The patent implements a feedback mechanism where the error microphone signal is continuously monitored and fed back to the adaptive filter. This feedback loop allows the system to detect when the output begins to diverge or produce audible artifacts, and automatically adjust the adaptive filter coefficients to restore stability. The feedback principle resolves the contradiction by enabling continuous adaptation while maintaining system stability through real-time monitoring and correction.

The patent employs dynamic adjustment of adaptive filter coefficients based on real-time system performance. Rather than using fixed coefficients, the system continuously adapts the filter parameters in response to changing acoustic conditions and system stability requirements. This dynamic approach allows the system to maintain effectiveness across varying operating conditions while preventing instability by adjusting coefficients when divergence is detected.

The patent implements dynamic control of the adaptation rate, allowing the system to use higher adaptation rates when noise conditions are stable and predictable, and reduce the adaptation rate when instability is detected. This dynamic adjustment of the learning rate parameter enables fast response to genuine noise changes while preventing divergence by slowing down adjustments when the system approaches instability thresholds.

The patent changes the adaptation parameter (learning rate) based on system performance and operating conditions. By dynamically adjusting this parameter, the system can achieve fast convergence when needed while maintaining stability during transient conditions or when approaching divergence. The parameter change principle allows the adaptation rate to be optimized in real-time rather than fixed at a single value.

The patent applies local quality monitoring by focusing distortion detection on specific critical parameters rather than analyzing the entire signal spectrum. The system monitors key indicators such as the error microphone signal magnitude, zero-crossing rate, and specific frequency components where instability is most likely to occur. This selective monitoring approach maintains high detection accuracy for instability while reducing overall computational complexity by ignoring less relevant signal aspects.

The patent extracts only the essential features needed for instability detection from the full error signal. Rather than processing the complete signal, the system extracts specific characteristics such as signal magnitude thresholds, zero-crossing patterns, and dominant frequency components that indicate instability. This extraction approach enables accurate distortion detection with reduced computational burden by focusing only on the most informative signal aspects.