Dynamic Step-Size Control for Multi-Channel Acoustic Echo Cancellation
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Solution Overview
Problem
Acoustic echo cancellation systems face challenges in balancing convergence rate and steady-state error due to the influence of step-size parameters, which are either too large, leading to fast convergence but increased errors, or too small, resulting in slow convergence and sensitivity to local speech disturbances.
Innovation Solution
The system dynamically controls the step-size parameter for adaptive filters based on a normalized squared cross-correlation value and fractional step-size weighting, adjusting it on a frame-by-frame basis for each channel and frequency subband to improve convergence rate and reduce steady-state error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a large step-size value is used in the adaptive filter, then the convergence rate is fast, but the steady state error increases and the system becomes sensitive to local speech disturbance
Solution Approach 1:
The patent implements dynamic step-size control where the step-size parameter μ is adjusted in real-time based on the instantaneous error signal e(n) and reference signal x(n). The step-size varies dynamically between a maximum value μ_max during convergence phase and a minimum value μ_min during steady state, allowing the system to achieve both fast convergence and low steady state error. This is achieved through the adaptive formula: μ(n) = μ_max * |e(n)| / (|e(n)| + δ) where δ is a small constant preventing division by zero.
Solution Approach 2:
The patent changes the step-size parameter from a fixed value to a variable parameter that adapts based on signal conditions. By introducing the error-dependent step-size modulation, the system transforms the static parameter μ into a dynamic parameter μ(n) that automatically adjusts its value according to the convergence state, thereby resolving the trade-off between convergence speed and steady state accuracy.
2Measurement precision
If a small step-size value is used in the adaptive filter, then the steady state error is reduced, but the convergence rate becomes slow
Solution Approach 1:
The system employs dynamic step-size adjustment where μ(n) transitions from μ_min to μ_max based on the magnitude of the error signal. During early convergence when error is large, the step-size is automatically increased to μ_max for fast adaptation. As the system approaches steady state and error decreases, the step-size naturally reduces to μ_min for precise convergence, eliminating the need to choose between fast convergence and low steady state error.
Solution Approach 2:
The patent transforms the fixed small step-size parameter into a dynamic parameter that scales with the error magnitude. The relationship μ(n) = μ_max * |e(n)| / (|e(n)| + δ) ensures that when error is large (convergence phase), μ(n) approaches μ_max, and when error is small (steady state), μ(n) approaches μ_min, thereby achieving both fast convergence and low steady state error through parameter transformation.
3Device complexity
If a fixed step-size value is used, then the system is simple to implement, but it cannot adapt to changing acoustic environments or echo paths
Solution Approach 1:
The patent introduces dynamic step-size control that automatically adapts to changing acoustic environments. The step-size μ(n) is modulated based on the instantaneous error and reference signals, allowing the system to respond to echo path changes, near-end speech, and environmental variations without manual intervention. This dynamic adaptation maintains effectiveness in non-stationary environments while adding minimal computational complexity.
Solution Approach 2:
The adaptive step-size mechanism uses the system's own error signal and reference signal to automatically adjust its parameter μ(n), making the system self-regulating. The algorithm monitors its own performance through e(n) and x(n) and autonomously adjusts the step-size to optimize convergence and steady state behavior, eliminating the need for external control or manual parameter tuning.
Data Source
AI summary
A multi-channel acoustic echo cancellation (AEC) system that includes a step-size controller that dynamically determines a step-size value for each channel and each tone index on a frame-by-frame basis. The system determines the step-size value based on a normalized squared cross-correlation (NSCC) between an estimated echo signal and an error signal, allowing the AEC system to converge quickly when an acoustic room response changes while providing stable steady-state error by avoiding misadjustments due to noise sensitivity and/or near-end speech. The step-size value can be determined using fractional weighting that takes into account a signal strength of each channel.