2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-20.2
Paper Title Blind Deinterleaving of Signals in Time Series with Self-attention Based Soft Min-cost Flow Learning
Authors Oğul Can, Yeti Z. Gürbüz, Middle East Technical University, Turkey; Berkin Yıldırım, ASELSAN A. Ş., Turkey; A. Aydın Alatan, Middle East Technical University, Turkey
SessionMLSP-20: Attention and Autoencoder Networks
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-SSEP] Source separation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We propose an end-to-end learning approach to address deinterleaving of patterns in time series, in particular, radar signals. We link signal clustering problem to min-cost flow as an equivalent problem once the proper costs exist. We formulate a bi-level optimization problem involving min-cost flow as a sub-problem to learn such costs from the supervised training data. We then approximate the lower level optimization problem by self-attention based neural networks and provide a trainable framework that clusters the patterns in the input as the distinct flows. We evaluate our method with extensive experiments on a large dataset with several challenging scenarios to show the efficiency.