Disentangling Modes and Interference in the Spectrogram of Multicomponent Signals

2025-03-19

AI Summary

In strong interference scenarios, modal components and interference become coupled in time-frequency spectrograms of multicomponent signals, severely degrading ridge detection accuracy. Method: This paper proposes a spectrogram decoupling framework that pioneers the integration of texture–geometry decomposition into time-frequency analysis. It establishes a dual-path architecture comprising a variational optimization model and a U-Net-based supervised learning network to separate intrinsic mode components from interference components in spectrograms. Furthermore, it introduces an interference-estimation-driven local adaptive window-length selection criterion, overcoming the limitations of fixed window lengths. Results: Evaluated on a synthetic multi-interference spectrogram dataset, the method significantly improves ridge localization accuracy (average gain of +27%), demonstrating both high precision and strong robustness. The two complementary pathways synergistically enhance overall performance.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• Decompose spectrogram into mode and interference parts
• Improve ridge detection in spectrograms with close modes
• Enhance time-frequency analysis under strong interference

Innovation

• Variational method for spectrogram decomposition
• U-Net architecture for supervised learning
• Adaptive window length for ridge detection