Signal Clustering With Class-Independent Segmentation

Stefano Gasperini; Magdalini Paschali; Carsten Hopke; David Wittmann; Nassir Navab

ICASSP 2020accepted0 citations

Signal Clustering With Class-Independent Segmentation

Stefano Gasperini, Magdalini Paschali, Carsten Hopke, David Wittmann, Nassir Navab

Abstract

Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches. In this paper we propose a Deep Learning-based clustering method, which encodes concurrent signals into images, and, for the first time, tackles clustering with image segmentation. Novel loss functions are introduced to optimize a Neural Network to separate the input pulses into pure and non-fragmented clusters. Outperforming a variety of baselines, the proposed approach is capable of clustering inputs directly with a Neural Network, in an end-to-end fashion.

BibTeX

@inproceedings{icassp2020_signalclustering,
  title = {Signal Clustering With Class-Independent Segmentation},
  author = {Stefano Gasperini and Magdalini Paschali and Carsten Hopke and David Wittmann and Nassir Navab},
  booktitle = {ICASSP 2020},
  year = {2020}
}