Acoustic Echo Cancellation Using Adaptive Confidence Parameters
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Solution Overview
Problem
Existing acoustic echo cancellation systems face challenges in achieving rapid tracking, low residual echo, robustness to near-end signals, and low complexity, especially in dynamic and spatial audio environments with multiple speakers and microphones, where algorithms like LMS, RLS, and APA have limitations such as poor convergence, high residual echo, and quadratic complexity.
Innovation Solution
The proposed solution involves an Incremental Maximum Likelihood (IML) algorithm with a confidence parameter that adapts based on the residual far-end to near-end ratio, allowing for fast convergence and low residual echo, and is implemented using two parallel filters with different confidence parameter settings to handle varying near-end activity levels, maintaining linear complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If LMS algorithm is used for echo cancellation, then the system complexity is low, but the convergence speed is poor
Solution Approach 1:
The patent segments the echo cancellation problem by using multiple parallel filters instead of a single filter. This allows different filters to operate with different step sizes, enabling fast convergence while maintaining low overall system complexity. Each filter handles a portion of the adaptation task independently.
Solution Approach 2:
The patent introduces dynamic step size adjustment where the step size varies over time based on the convergence state. The step size is large initially for fast convergence and then reduced for fine-tuning, making the adaptation process dynamic rather than static.
2Speed
If RLS algorithm is used for echo cancellation, then the convergence speed is fast, but the computational complexity becomes quadratic
Solution Approach 1:
The patent divides the complex adaptation task into multiple simpler parallel filters, each performing lightweight updates. This segmentation avoids the quadratic complexity of RLS while achieving fast convergence through the combined effect of multiple filters with appropriate step sizes.
Solution Approach 2:
The patent uses multiple simple, computationally inexpensive filters instead of one complex filter. Each filter is computationally 'cheap' but collectively they achieve the performance of more expensive algorithms like RLS.
3Speed
If APA algorithm is used for echo cancellation, then the convergence speed is fast, but the residual echo remains high
Solution Approach 1:
The patent dynamically adjusts the step size during the adaptation process, using large step sizes for fast initial convergence and then reducing the step size to minimize residual echo. This dynamic adjustment allows the system to achieve both fast convergence and low residual echo.
Solution Approach 2:
The patent implements periodic updates of the filter coefficients with varying step sizes. This periodic action with changing parameters allows the system to converge quickly initially and then refine the solution to reduce residual echo.
4Speed
If the step size is increased for fast convergence, then the convergence speed improves, but the robustness to near-end signals deteriorates
Solution Approach 1:
The patent segments the adaptation process into multiple parallel filters that can use different step sizes. This allows the system to combine fast convergence from large step sizes with robustness from small step sizes, achieving both goals simultaneously through the segmented architecture.
Solution Approach 2:
The patent changes the step size parameter dynamically based on the convergence state and signal conditions. By adjusting this critical parameter, the system achieves fast convergence when needed and maintains robustness when near-end signals are present.
Data Source
AI summary
Echo cancellation for a two-way audio communication includes receiving, at an AEC system from microphone(s), an audio signal based on, at least in part, near-end signals and reproduced far-end signals. Loudspeaker(s) reproduced the far-end signals. The AEC system is operated, at least in part, with filter(s) so as to update estimates of coefficients of an acoustic channel from the loudspeaker(s) to the microphone(s). Control parameter(s) affecting an operation of the AEC system that is/are configurable and is/are set to value(s), from a range of values, is/are determined, based on estimating an accuracy of the estimates of the coefficients of the acoustic channel and a characteristic of the near-end signals. The AEC system controls the filter(s) with different values of the control parameter(s) at different times.