FIR Filter Coefficient Updating for Time-Varying Echo Paths
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Solution Overview
Problem
The pre-defined step-size diagonal matrix in the exponentially weighted step-size NLMS algorithm fails to track the time-varying room impulse response, leading to degraded performance and decreased convergence rate in acoustic echo cancellation systems.
Innovation Solution
An updated step-size diagonal matrix is obtained by estimating the attenuation factor based on the coefficient vector of the FIR filter, allowing for adaptive recalibration of the step-size diagonal matrix during pre-defined updating periods, which improves the algorithm's ability to track changing environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a pre-defined step-size diagonal matrix is used in the ES-NLMS algorithm, then the algorithm structure is simple and easy to implement, but it cannot track the time-varying characteristic of the room impulse response, resulting in degraded performance and decreased convergence rate
Solution Approach 1:
The patent applies the dynamics principle by transforming the static pre-defined step-size diagonal matrix into a dynamic one that is updated periodically. The step-size diagonal matrix is recalculated at specific updating moments based on current coefficient vectors, enabling it to adapt to time-varying room impulse response characteristics while maintaining a structured update mechanism that balances complexity and performance
Solution Approach 2:
The patent implements parameter changes by modifying the step-size diagonal matrix parameters (α and γ) from fixed pre-defined values to dynamically calculated values. The attenuation factor γ is estimated based on the decay rate of coefficient vectors, and these parameters are periodically updated to match the changing statistical characteristics of the room impulse response, thereby improving tracking capability
2Adaptability or versatility
If the step-size diagonal matrix is updated frequently to track time-varying characteristics, then the adaptability and convergence rate improve, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies periodic action by updating the step-size diagonal matrix at specific updating moments rather than continuously. The updating period is determined based on the statistical characteristics of the room impulse response, performing calculations only when necessary to track changes in environmental conditions, thus reducing unnecessary computational overhead while maintaining effective adaptation
Solution Approach 2:
The patent implements preliminary action by pre-defining updating moments based on the statistical characteristics of room impulse response before actual updates are performed. The updating period and timing are predetermined according to expected environmental change rates, allowing the system to prepare for updates in advance and execute them efficiently when needed, rather than reacting to changes in real-time
Data Source
AI summary
A method and a device for updating a coefficient vector of a finite impulse response filter are provided. The update method includes: obtaining an updated step-size diagonal matrix for a coefficient vector of the FIR filter; and obtaining an updated coefficient vector of the FIR filter based on the updated step-size diagonal matrix.