用Transformer分解一维信号，分离出恒定、平滑、纹理和噪声成分。
Additive decomposition of one-dimensional signals using Transformers
- 基于Transformer架构，将信号拆分为四类成分。
- 在合成数据上分解准确率高，可复现真实信号特征。
- 适合需要精细信号解析的科研与工程场景。
一维信号分解是多个科学领域广泛使用的重要预处理技术。传统方法依赖数学模型，而最新研究显示，将深度学习模型应用于该问题具有巨大潜力。本文提出一种新型一维信号加性分解方法，利用Transformer架构将信号分解为分段常数、平滑（低频振荡）、纹理（高频振荡）及噪声四类成分。模型在合成数据上训练，实验结果表明其对同分布输入信号的建模与分解表现优异，具备高精度与强泛化能力。
原文摘要 · Abstract (English)
One-dimensional signal decomposition is a well-established and widely used technique across various scientific fields. It serves as a highly valuable pre-processing step for data analysis. While traditional decomposition techniques often rely on mathematical models, recent research suggests that applying the latest deep learning models to this problem presents an exciting, unexplored area with promising potential. This work presents a novel method for the additive decomposition of one-dimensional signals. We leverage the Transformer architecture to decompose signals into their constituent components: piece-wise constant, smooth (low-frequency oscillatory), textured (high-frequency oscillatory), and a noise component. Our model, trained on synthetic data, achieves excellent accuracy in modeling and decomposing input signals from the same distribution, as demonstrated by the experimental results.
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