Disentangling Modes and Interference in the Spectrogram of Multicomponent Signals
This work addresses interference issues in time-frequency analysis for signal processing applications, representing an incremental improvement.
The paper tackles the problem of decomposing spectrograms of multicomponent signals into mode and interference parts using a variational method and a supervised U-Net approach, with results showing improved ridge detection in time-frequency analysis.
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.