Attention-Based Signal Alignment for Variable-Delay Echo Cancellation
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Solution Overview
Problem
Conventional machine learning techniques for processing time-varying signals, such as audio or video, struggle with accurately aligning signals when delays are unpredictable, leading to poor performance in echo cancellation and other enhancement processes.
Innovation Solution
Utilizing an attention layer within a machine learning model, such as a deep neural network, to perform alignment and enhancement jointly, allowing for 'soft' alignment through weighted delay estimates rather than relying on external delay modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional machine learning techniques are used for processing time-varying signals, then the processing can be performed with simpler methods, but the signal alignment accuracy deteriorates when delays are unpredictable
Solution Approach 1:
The patent combines signal alignment and signal enhancement into a single joint processing framework. The attention mechanism aligns signals by computing weighted sums of delayed versions of the first signal, while the enhancement network processes the aligned signals to remove echoes and noise. This merging of alignment and enhancement operations resolves the contradiction by achieving high alignment accuracy through the attention mechanism while maintaining a unified processing architecture.
Solution Approach 2:
The attention mechanism serves as an intermediary component that computes alignment weights for different delayed versions of the first signal. Instead of using fixed delay estimates from external modules, the attention mechanism dynamically determines the contribution of each delayed signal based on the current input, acting as a mediator between the raw signals and the enhancement network. This intermediary approach improves alignment accuracy for unpredictable delays.
2Ease of operation
If external delay modules are used for signal alignment, then the alignment process can be simplified, but the adaptability to variable delays deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by using an attention mechanism that computes time-varying alignment weights based on the current input signal characteristics. Instead of relying on fixed delay estimates from external modules, the attention mechanism dynamically adjusts the weights applied to different delayed versions of the first signal. This dynamic approach maintains ease of operation through automated weight computation while achieving high adaptability to variable and unpredictable delays.
Solution Approach 2:
The attention mechanism changes the alignment parameters (weights for different delays) based on the current input conditions. Rather than using fixed delay values from external modules, the system computes optimal weights dynamically by analyzing the relationship between the first and second signals. This parameter change approach allows the system to adapt to variable delays while maintaining a relatively simple operational framework.
3Measurement precision
If joint alignment and enhancement processing is performed using attention mechanism, then the signal alignment accuracy improves for variable delays, but the device complexity increases
Solution Approach 1:
The attention mechanism performs multiple functions within a single computational framework: it aligns signals by computing weighted sums of delayed versions, and it provides adaptive weighting for different delay scenarios. The same attention-based alignment output is then fed to the enhancement network for echo and noise removal. This multi-functionality achieves high alignment accuracy while avoiding the need for separate alignment and enhancement modules, thereby managing complexity.
Solution Approach 2:
The attention mechanism is self-adaptive, automatically computing the optimal alignment weights based on the input signals without requiring external delay estimation modules or manual configuration. The mechanism serves itself by learning the appropriate weights during training and applying them during inference. This self-service capability improves alignment accuracy for variable delays while keeping the system architecture relatively simple by eliminating external dependency modules.
Data Source
AI summary
This document relates to alignment of time-varying signals, such as sound. One example includes a method or technique that can be performed on a computing device. The method or technique can include inputting a first time-varying signal and a second time-varying signal into a trained model. The method or technique can also include performing temporal alignment of the first time-varying signal to the second time-varying signal using an attention layer of the trained model to obtain a temporally-aligned first time-varying signal. The method or technique can also include enhancing the second time-varying signal based at least on the temporally-aligned first time-varying signal, and outputting the enhanced second time-varying signal.