Learnable Kalman Filter for Nonlinear Acoustic Echo Cancellation
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Solution Overview
Problem
Conventional AEC algorithms struggle with nonlinearity modeling and require tuning of control parameters, leading to limitations in fast convergence and effectiveness in double-talk scenarios, while deep learning-based methods fail to handle continuously changing echo paths effectively.
Innovation Solution
A hybrid method combining a frequency domain Kalman filter (FDKF) with a deep neural network (DNN) to estimate nonlinear distortions and transition factors, enhancing the Kalman filter's performance in acoustic echo cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional adaptive filtering algorithms (NLMS, RLS) are used for AEC, then echo removal can be achieved through linear transfer function estimation, but nonlinearity modeling is missing and control parameters require tuning
Solution Approach 1:
The patent merges conventional Kalman filter algorithms with deep neural networks to create a hybrid system that combines the strengths of both approaches. The Kalman filter provides robustness in double-talk scenarios and fast convergence, while the DNN component handles nonlinearity modeling that was missing in conventional approaches.
Solution Approach 2:
The patent introduces a learnable Kalman gain as an intermediary mechanism that adapts the filtering process dynamically. This learnable parameter allows the system to automatically adjust to changing acoustic environments without requiring manual tuning of control parameters, while maintaining the ability to model nonlinearities through the DNN component.
2Device complexity
If deep learning-based methods are used for AEC, then nonlinear relations can be modeled effectively, but continuously changing echo paths are not handled well
Solution Approach 1:
The patent implements a dynamic adaptive filtering system where the Kalman gain is learned through neural networks and continuously updated based on incoming data. This dynamic adaptation allows the system to track continuously changing echo paths while maintaining the ability to model nonlinearities, resolving the limitation of static DNN-based approaches.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors performance and adjusts the learnable Kalman gain accordingly. This feedback loop enables the system to adapt to continuously changing acoustic environments while maintaining accurate nonlinearity modeling, addressing the lack of adaptability in conventional DNN methods.
3Speed
If conventional adaptive filtering algorithms are used, then control parameters need to be tuned for fast convergence, but this tuning requirement limits effectiveness
Solution Approach 1:
The patent implements a self-service system where the learnable Kalman gain automatically adapts to changing conditions through neural network learning. This eliminates the need for manual parameter tuning while maintaining fast convergence, as the system learns optimal filtering parameters from the data itself rather than requiring pre-configured control parameters.
4Reliability
If hybrid methods based on Kalman filter are used in other domains (pose estimation, speech filtering), then good results are achieved, but these methods have not been well explored in AEC domain
Solution Approach 1:
The patent develops a universal hybrid Kalman filter framework that can be applied across multiple domains including pose estimation, speech filtering, and AEC. The learnable Kalman gain mechanism provides a domain-agnostic approach that adapts to different applications, with the DNN component being task-specific. This multi-functionality allows the same core architecture to serve various signal processing challenges.
Data Source
AI summary
A method and apparatus comprising computer code configured to cause a processor or processors to receive an audio signal obtained from a microphone, input the audio signal into a neural-network based AEC model, and output an AEC signal from the neural-network based AEC model in which AEC is applied to the audio signal, and the AEC signal is a version of the audio signal in which acoustic echo noise of the audio signal is suppressed and target audio of the audio signal is sustained.