Time-Frequency Filtering Meets Graph Clustering
This work addresses signal processing challenges by applying graph clustering techniques, but it appears incremental as it adapts existing methods to a new context without claiming major breakthroughs.
The paper tackles the problem of identifying signal components from time-frequency representations by reformulating it as a graph clustering problem, where clusters are strongly connected subgraphs with few interconnections, and demonstrates this approach through numerical experiments.
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.