Low-Complexity Neural Echo Cancellation for Real-Time Audio
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Solution Overview
Problem
Existing audio processing systems face challenges in effectively canceling acoustic echoes in real-time video conferencing and audio calls, particularly due to the complexity of echo path estimation and the need for non-linear filters.
Innovation Solution
A low-complexity acoustic echo cancellation system using a neural network-based approach that directly estimates clear speech without echo, eliminating the need for echo path estimation and non-linear filters, and is trained using gradient-based optimization to minimize loss functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional echo path estimation and non-linear filters are used for acoustic echo cancellation, then echo cancellation capability is achieved, but system complexity increases
Solution Approach 1:
The patent extracts and removes the complex echo path estimation and non-linear filter components from the traditional AEC system. By directly estimating clean speech without requiring these intermediate components, the system achieves echo cancellation while significantly reducing complexity. This is accomplished through a simplified model that bypasses the traditional multi-stage processing chain.
Solution Approach 2:
The patent segments the complex echo cancellation problem into manageable components by using a neural network that processes audio signals in a structured manner. The system divides the input audio into relevant features, processes them through multiple layers of the neural network, and reconstructs the clean speech signal, achieving cancellation without the full complexity of traditional methods.
2Speed
If real-time processing is implemented for acoustic echo cancellation, then responsiveness is improved, but computational resource requirements increase
Solution Approach 1:
The patent changes the parameters of the processing system by using a neural network architecture optimized for real-time operation. The system processes audio signals through efficiently designed network layers that maintain real-time performance while reducing overall computational burden compared to traditional methods. The parameter optimization enables faster processing with lower resource consumption.
3Device complexity
If neural network-based approach is used for echo cancellation, then processing complexity is reduced, but training requirements are introduced
Solution Approach 1:
The patent applies preliminary action by training the neural network offline before deployment. The training phase prepares the model with learned parameters and weights, so that during real-time operation, the system can perform echo cancellation without requiring additional training time. This separates the complex learning process from the real-time processing requirement.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media relate to a method for acoustic echo cancellation. The system inputs one or more signal representations into an acoustic echo cancellation network comprising one or more network blocks to generate a mask, each network block comprising one or more convolutional blocks, each convolutional block comprising one or more neural networks. The system combines the mask and a near-end audio signal representation to generate an echo-cancelled audio signal representation. The system generates an echo-cancelled audio signal based on the echo-cancelled audio signal representation.