Time-Frequency Filtering Meets Graph Clustering
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Abstract (arXiv)
We show that the problem of identifying different signal components from a time-frequency representation can be equivalently phrased as a graph clustering problem: given a graph G=(V,E) one aims to identify `clusters', subgraphs that are strongly connected and have relatively few connections between them. The graph clustering problem is well studied, we show how these ideas can suggest (many) new ways to identify signal components. Numerical experiments illustrate the ideas.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 8 Oct 2025.
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References · 8
- 2017Community Detection and Stochastic Block Modelscited by 319
- 2014Exact Recovery in the Stochastic Block Modelcited by 78
- 2017A Survey on Multi-View Clusteringcited by 32
- 2020High-Order Synchrosqueezing Transform for Multicomponent Signals Analysis -- With an Application to Gravitational-Wave Signalcited by 2
- 2022Unsupervised classification of the spectrogram zeroscited by 2
- 2017On the zeros of the spectrogram of white noisecited by 1
- 2025Uncertainty quantification of synchrosqueezing transform under complicated nonstationary noisecited by 1
- 2025Time-Frequency Mode Decomposition for Wind Turbine Vibration Monitoring under Variable Speed Operationcited by 1
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