Spatial Filter Non-Linear Echo Cancellation
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Solution Overview
Problem
Conventional Acoustic Echo Cancellers (AECs) in audio processing systems struggle to effectively cancel non-linear echoes due to their inability to model non-linearities, which are computationally expensive and difficult to estimate, leading to residual echoes that limit the performance of speech recognition systems.
Innovation Solution
The implementation of a spatial filter to capture and track non-linearities in the audio processing system, allowing a linear AEC to cancel non-linear echoes with reduced computational complexity and improved accuracy, without requiring retuning as conditions change over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional linear filters are used in AEC to cancel acoustic echo, then the system complexity is low and computational cost is reduced, but the ability to model and cancel non-linear echoes is insufficient, leading to residual non-linear echo in the target speech signal
Solution Approach 1:
The patent segments the echo cancellation task into two parts: a linear filter handles the linear echo component, while a separate non-linear model (using polynomial or neural network functions) handles the non-linear echo component. This segmentation allows each component to be optimized independently, resolving the contradiction between cancellation performance and modeling complexity.
Solution Approach 2:
The patent creates a composite echo cancellation system that combines linear filtering techniques with non-linear modeling techniques (polynomial functions or neural networks). This composite approach integrates the strengths of both linear and non-linear methods, achieving superior echo cancellation performance while managing computational complexity through efficient algorithm design.
2Measurement precision
If non-linear models are used to estimate non-linearities in the acoustic signal, then the echo cancellation accuracy is improved, but the computational cost becomes very expensive and difficult to estimate in real-time
Solution Approach 1:
The patent changes the parameters of the non-linear model by using polynomial functions with limited order or pre-trained neural network weights, which reduces the number of parameters that need to be estimated in real-time. This allows accurate non-linear echo cancellation while maintaining real-time processing capability, resolving the contradiction between accuracy and processing speed.
Solution Approach 2:
The patent performs preliminary modeling of the non-linear characteristics during system calibration or offline training, storing the results in lookup tables or pre-trained neural network weights. During real-time operation, the system only needs to retrieve and apply these pre-computed values, dramatically reducing computational cost while maintaining high cancellation accuracy.
3Adaptability or versatility
If the AEC system adapts to changing non-linearities over time, then the echo cancellation remains effective under varying conditions, but the system requires frequent retuning and increases operational complexity
Solution Approach 1:
The patent implements feedback mechanisms where the AEC system continuously monitors the residual echo and automatically adjusts the non-linear model parameters or triggers retuning when performance degradation is detected. This feedback-driven adaptation maintains effectiveness under varying conditions while reducing manual intervention, resolving the contradiction between adaptability and operational simplicity.
Data Source
AI summary
Techniques for non-linear acoustic echo cancellation are described herein. In an embodiment, a system comprises a loudspeaker, a microphone array, a spatial filtering logic with a spatial filter, an acoustic echo canceller (AEC) logic and an adder logic block. The spatial filtering logic is configured to generate a spatially-filtered signal by applying the spatial filter using a reference signal sent to the loudspeaker and a multi-channel microphone signal from the microphone array. The generated spatially-filtered signal carries both linear echo and non-linear echo that are included in the multi-channel microphone signal. The AEC logic is configured to apply a linear adaptive filter using the spatially-filtered signal to generate a cancellation signal that estimates both the linear echo and the non-linear echo of the multi-channel microphone signal. The adder logic block is configured to generate an output signal based on the cancellation signal.