|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. (bib)
|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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