Disentangling Modes and Interference in the Spectrogram of Multicomponent Signals
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909542511542272 |
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| author | Polisano, Kévin Meignen, Sylvain Laurent, Nils Leterme, Hubert |
| author_facet | Polisano, Kévin Meignen, Sylvain Laurent, Nils Leterme, Hubert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14990 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Disentangling Modes and Interference in the Spectrogram of Multicomponent Signals Polisano, Kévin Meignen, Sylvain Laurent, Nils Leterme, Hubert Computer Vision and Pattern Recognition Signal Processing 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. |
| title | Disentangling Modes and Interference in the Spectrogram of Multicomponent Signals |
| topic | Computer Vision and Pattern Recognition Signal Processing |
| url | https://arxiv.org/abs/2503.14990 |