Adaptive Filter Transfer Function Phase Correction in Active Noise Control
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Solution Overview
Problem
Active noise control systems face challenges in dynamically adapting to changes in the environment, leading to instability and reduced noise cancellation efficiency, particularly in dynamic settings like vehicles where acoustic changes occur frequently.
Innovation Solution
The method involves identifying and adjusting the phase error of the adaptive filter's transfer function based on the trajectory of its coefficients, allowing for self-tuning and improved convergence without compromising stability, by updating the model of the secondary path and adjusting the phase to account for changes in the environment, transducers, and sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the adaptive filter uses fixed coefficients, then the system is stable, but the noise cancellation efficiency decreases in dynamic environments
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating the adaptive filter coefficients based on real-time error signals from the microphone array. The system transitions from static to dynamic operation by employing an adaptive algorithm that adjusts filter parameters online, allowing the noise cancellation system to track and respond to changing acoustic environments while maintaining stability through controlled adaptation rates.
2Adaptability or versatility
If the adaptive filter coefficients are frequently updated, then the noise cancellation efficiency improves, but the system stability deteriorates
Solution Approach 1:
The system employs feedback control by using the error signal from the error microphone as input to the adaptive filter algorithm. This feedback loop allows the system to continuously monitor cancellation performance and adjust coefficients accordingly, achieving stable adaptation through closed-loop control that prevents excessive or unstable updates.
Solution Approach 2:
The patent implements controlled parameter changes by adjusting the adaptive filter coefficients according to a specific adaptation algorithm that modifies filter parameters based on the error signal characteristics. The system changes filter parameters dynamically while controlling the rate and magnitude of changes to maintain stability, using techniques such as normalized adaptation that scales updates based on signal energy.
3Adaptability or versatility
If manual tuning is performed frequently, then the noise cancellation efficiency is optimized, but the complexity of operation increases
Solution Approach 1:
The system implements self-service by automatically adapting its filter coefficients without requiring manual intervention. The adaptive algorithm autonomously monitors the acoustic environment through the microphone array, computes optimal filter parameters, and updates the noise cancellation system in real-time, eliminating the need for frequent manual tuning while maintaining optimal performance.
Data Source
AI summary
The technology described in this document can be embodied in a computer-implemented method that includes receiving, at one or more processing devices, a plurality of values representing a set of coefficients of an adaptive filter over a period of time, and identifying, by the one or more processing devices based on the plurality of values, a phase error associated with a transfer function of the adaptive filter. The method also includes adjusting, based on the identified phase error, a phase associated with the transfer function of the adaptive filter such that coefficients calculated using the adjusted transfer function reduce the phase error. The method further includes determining a set of coefficients for the adaptive filter based on the adjusted transfer function, and programming the adaptive filter with the determined set of coefficients to enable operation of the adaptive filter.