Time-Frequency Filtering Meets Graph Clustering
Signal Processing
2025-10-10 v1 Machine Learning
Abstract
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 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.
Cite
@article{arxiv.2510.07503,
title = {Time-Frequency Filtering Meets Graph Clustering},
author = {Marcelo A. Colominas and Stefan Steinerberger and Hau-Tieng Wu},
journal= {arXiv preprint arXiv:2510.07503},
year = {2025}
}