Volterra Filter Nonlinear Echo Suppression via MTLS Adaptation
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Solution Overview
Problem
Existing nonlinear acoustic echo signal suppression technologies, such as those using cascade structures and power filters, struggle with fast adaptation to abrupt environmental variations and nonlinearity, particularly in Volterra filters which rely on fixed constants and adaptive algorithms like NLMS.
Innovation Solution
The implementation of a Multi-Tap Least Squares (MTLS) estimator to estimate Volterra filter factors, combined with a data-driven algorithm for estimating near-end speech presence probability ratios, enables quick adaptation to environmental changes and suppresses nonlinear acoustic echo signals using a gain function based on a statistical model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Volterra filter uses fixed constants and adaptive algorithms like NLMS, then the filter can model nonlinear acoustic echo signals, but it is difficult to adapt quickly to abrupt variations of environment and nonlinearity
Solution Approach 1:
The patent transitions from static fixed constants in traditional Volterra filters to dynamic adaptive filter factors estimated by MTLS. The filter factors are continuously updated based on current environmental conditions, allowing the system to adapt dynamically to abrupt variations while maintaining reliable nonlinear modeling through the statistical rigor of least squares estimation
Solution Approach 2:
The patent changes the parameters from fixed constants to variable filter factors that are estimated adaptively using MTLS. This parameter transformation enables the Volterra filter to adjust its characteristics in response to environmental changes, achieving both fast adaptation and reliable performance through data-driven parameter optimization
2Reliability
If cascade structure or power filter is used for nonlinear acoustic echo signal estimation, then the system complexity is reduced, but the performance is inferior compared to Volterra filter
Solution Approach 1:
The patent enhances the Volterra filter's capability to handle diverse nonlinear acoustic echo scenarios through MTLS-based adaptive estimation. This universal approach allows the same filter structure to effectively model various nonlinearities and environmental conditions, achieving superior performance across different applications without requiring complex alternative structures
3Ease of operation
If fixed constants are used in Volterra filter, then the filter implementation is simplified, but it cannot provide adaptation to circumferential environments until speech signal is input
Solution Approach 1:
The patent applies preliminary action by estimating filter factors using MTLS before speech signals are processed. The system pre-adapts to environmental conditions by continuously estimating filter factors from available data, ensuring that the Volterra filter is already adapted to circumferential environments before actual speech processing begins, eliminating the waiting period inherent in fixed constant approaches
Data Source
AI summary
A nonlinear acoustic echo signal suppression system and method using a Volterra filter is disclosed. The nonlinear acoustic echo signal suppression system includes an acoustic echo signal estimator configured to estimate a nonlinear acoustic echo signal by using a Volterra filter in a frequency filter, and a near-end talker speech signal generator configured to generate a near-end talker speech signal, in which the nonlinear acoustic echo signal is suppressed, by using a gain function based on a statistical model.