Echo Cancellation Step Size Control for Reliable Filter Convergence
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Solution Overview
Problem
Existing echo cancellation systems face challenges in reliably and efficiently adapting prediction filters under various signal and environmental conditions, particularly in the presence of noise, leading to uncertain convergence and inefficiencies.
Innovation Solution
The method employs gradient descent with controlled adaptation step size based on the direction and predictability of the gradient, using normalization and smoothing of gradient vectors to ensure robust and rapid convergence of filter coefficients, even in noisy environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional adaptation mechanisms are used to adapt prediction filters, then the system can operate under various signal conditions, but the adaptation convergence is uncertain and may not reliably or rapidly converge under some conditions
Solution Approach 1:
The patent applies dynamics by making the adaptation step size variable rather than fixed. The step size dynamically adjusts based on the estimated noise power spectrum, allowing the adaptation process to accelerate when conditions are favorable and slow down when noise interferes, thereby achieving both rapid convergence and reliable performance across varying signal conditions
Solution Approach 2:
The patent changes the parameter of adaptation step size based on noise conditions. By estimating the noise power spectrum and using it to modulate the step size, the system adapts its convergence behavior to match environmental conditions, ensuring reliable and efficient adaptation whether noise levels are high or low
2Adaptability or versatility
If adaptation is performed under noisy conditions, then echo cancellation must handle various environmental conditions, but conventional methods introduce uncertainty and may not converge reliably
Solution Approach 1:
The patent implements feedback by continuously estimating the noise power spectrum and using this estimation to adjust the adaptation step size. This closed-loop approach allows the system to respond to changing noise conditions in real-time, maintaining reliable convergence across diverse signal and environmental conditions
Solution Approach 2:
The system changes the adaptation parameter (step size) based on noise level detection. When noise is detected, the step size is reduced to prevent divergence; when noise is low, the step size increases to accelerate convergence, thereby maintaining adaptability and reliability simultaneously
Data Source
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AI summary
In some embodiments, an echo cancellation method which includes adaptation of at least one prediction filter, with adaptation step size controlled using gradient descent on a set of filter coefficients of the filter, where control of the adaptation step size is based at least in part on a direction of adaptation and a predictability of a gradient of adaptation (e.g., a gradient vector). Other aspects of embodiments of the invention include systems, methods, and computer program products for controlling adaptation step size of adaptive (e.g., low-complexity adaptive) echo cancellation. In some embodiments, adaptation step size control is based on a normalized, scaled gradient of adaptation, or includes smoothing of a normalized gradient of adaptation