分离信号谱图中的成分与干扰，提升近似模式的时频分析精度。
Disentangling Modes and Interference in the Spectrogram of Multicomponent Signals
- 用变分法和U-Net网络分离谱图中的成分与干扰项。
- 通过识别干扰，自适应调整窗口长度，改善密集模式的脊线检测。
- 适用于强干扰环境下需要精准时频分析的研究者。
本文研究多分量信号谱图的分解问题，将其拆分为成分部分与干扰部分。提出两种方法：(i) 受图像纹理-几何分解启发的变分方法；(ii) 在包含多样干扰模式和噪声条件的数据集上训练的U-Net监督学习方法。一旦识别出干扰成分，即可据此设计局部自适应窗长准则，以增强在密集模式下的脊线检测能力。数值实验展示了两种方法在谱图分解上的优劣，凸显其在强干扰条件下提升时频分析性能的潜力。
原文摘要 · Abstract (English)
In this paper, we investigate how the spectrogram of multicomponent signals can be decomposed into a mode part and an interference part. We explore two approaches: (i) a variational method inspired by texture-geometry decomposition in image processing, and (ii) a supervised learning approach using a U-Net architecture, trained on a dataset encompassing diverse interference patterns and noise conditions. Once the interference component is identified, we explain how it enables us to define a criterion to locally adapt the window length used in the definition of the spectrogram, for the sake of improving ridge detection in the presence of close modes. Numerical experiments illustrate the advantages and limitations of both approaches for spectrogram decomposition, highlighting their potential for enhancing time-frequency analysis in the presence of strong interference.
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