Signal Clustering with Class-Independent Segmentation
Gasperini S, Paschali M, Hopke C, Wittmann D, Navab N (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2020-May
Pages Range: 3982-3986
Conference Proceedings Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Event location: Barcelona, ESP
ISBN: 9781509066315
DOI: 10.1109/ICASSP40776.2020.9053409
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.
Involved external institutions
Germany (DE)How to cite
APA:
Gasperini, S., Paschali, M., Hopke, C., Wittmann, D., & Navab, N. (2020). Signal Clustering with Class-Independent Segmentation. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 3982-3986). Barcelona, ESP: Institute of Electrical and Electronics Engineers Inc..
MLA:
Gasperini, Stefano, et al. "Signal Clustering with Class-Independent Segmentation." Proceedings of the 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, Barcelona, ESP Institute of Electrical and Electronics Engineers Inc., 2020. 3982-3986.
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