Echo Cancellation Time Delay Estimation Using Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing time delay estimation (TDE) methods in communication systems face challenges with inaccurate delay estimation due to poor acoustic linearity and low correlation between reference and echo signals, particularly in personal PCs and handheld devices, leading to prolonged processing times for accurate delay estimation.
Innovation Solution
A method utilizing a time delay estimation model comprising convolutional neural networks and temporal convolutional networks to estimate the time difference between reference and echo signals, independent of signal linearity, combined with signal preprocessing to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional correlation-based TDE methods are used, then the method is simple to implement, but the measurement precision deteriorates due to poor acoustic linearity and low signal correlation
Solution Approach 1:
The patent replaces traditional correlation-based mechanical signal processing methods with deep learning neural network models. The neural network learns complex non-linear relationships between reference and echo signals, substituting the conventional correlation calculation approach with a data-driven model that can handle non-linear acoustic distortions effectively.
Solution Approach 2:
The patent transforms the signal processing approach by changing from direct correlation calculation to neural network feature extraction. The neural network processes signal parameters (amplitude, phase, frequency) through multiple layers, transforming raw signals into meaningful features that capture time delay information even under non-linear conditions.
2Productivity
If traditional correlation-based TDE methods are used, then the processing is computationally efficient, but the reliability deteriorates in non-linear acoustic environments
Solution Approach 1:
The patent performs preliminary training of neural network models using extensive datasets of reference and echo signals. This preliminary action creates pre-trained models that can directly estimate time delays in real-time applications without requiring complex runtime calculations, ensuring reliable performance in non-linear acoustic environments while maintaining processing efficiency.
Solution Approach 2:
The patent substitutes traditional iterative correlation-based processing with a single-pass neural network inference system. The neural network model, after training, can predict time delays directly from input signals without requiring multiple iterations or accumulations, improving both reliability and processing speed.
3Measurement precision
If signal accumulation is performed to improve accuracy, then the measurement precision improves, but the loss of time increases significantly
Solution Approach 1:
The patent performs the computationally intensive work of learning signal relationships during the offline training phase, where extensive data accumulation can be performed without time constraints. The trained model then captures these accumulated insights in compact parameters, enabling fast real-time inference without requiring continuous signal accumulation during actual operation.
Solution Approach 2:
The patent replaces the mechanical accumulation process with neural network inference. Instead of accumulating correlation values over time to improve accuracy, the pre-trained neural network directly outputs accurate time delay estimates from single or few input frames, eliminating the time-consuming accumulation process while maintaining or improving precision.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
A method and apparatus for processing a signal. An implementation of the method includes: acquiring a reference signal of a to-be-tested voice, the reference signal being a signal output to a voice output device, where the voice output device outputs the to-be-tested voice after obtaining the reference signal; receiving, from a voice input device, an echo signal of the to-be-tested voice, the echo signal being a signal of the to-be-tested voice collected by the voice input device; performing signal preprocessing on the reference signal and the echo signal respectively; and inputting the processed reference signal and the processed echo signal into a pre-trained time delay estimation model, to obtain a time difference between the reference signal and the echo signal output by the time delay estimation model. ---