Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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S. Gasperini, M. Paschali, C. Hopke, D. Wittmann, N. Navab
Signal Clustering with Class-independent Segmentation
45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2020.
The original publication is available currently online on arXiv.

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.
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