Y-Net: A Dual Path Model for High Accuracy Blind Source Separation
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Beitragende
Abstract
Industrial IoT (IIoT) in conjunction with Ultra-Reliable Low-Latency Communications (URLLC) often struggles with data-rich, information-poor contexts. Blind Source Separation (BSS) is one of the key technologies which can obtain the desired high-value information from all of the observed raw sensory data. As shown by recent studies, BSS can be both fast enough for low-latency requirements and sufficiently accurate to be a reliable method in large IoT deployments. Nonetheless, the trade-off between signal context usage and data recovery accuracy often affects the separation quality of BSS. In this paper, we propose for the first time a novel dual path convolutional neural network model, called Y-Net, for high accuracy BSS. Specifically, the separation quality is improved by the parallel perception and joint combination of both high-and low-level features of input signals, which we demonstrated through extensive numerical evaluations. In particular, Y-Net improves the Source-to-Distortion Ratio by 2.70% to 35.32% for different target signals, while the model size is only slightly increased, compared to other current solutions.
Details
Originalsprache | Englisch |
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Titel | 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
ISBN (elektronisch) | 978-1-7281-7307-8 |
Publikationsstatus | Veröffentlicht - Dez. 2020 |
Peer-Review-Status | Ja |
Konferenz
Titel | 2020 IEEE Globecom Workshops, GC Wkshps 2020 |
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Dauer | 7 - 11 Dezember 2020 |
Stadt | Virtual, Taipei |
Land | Taiwan |
Externe IDs
Scopus | 85102957228 |
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Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- algorithm optimization, blind source separation, data analysis, industry 4.0, neural network, URLLC