@inproceedings{842e2383d8b1453cb00a473d86ed3e8e,
title = "Multi-view networks for denoising of arbitrary numbers of channels",
abstract = "We propose a set of denoising neural networks capable of operating on an arbitrary number of channels at runtime, irrespective of how many channels they were trained on. We coin the proposed models multi-view networks since they operate using multiple views of the same data. We explore two such architectures and show how they outperform traditional denoising models in multi-channel scenarios. Additionally, we demonstrate how multi-view networks can leverage information provided by additional recordings to make better predictions, and how they are able to generalize to a number of recordings not seen in training.",
keywords = "Deep learning, Denoising, Multichannel",
author = "Jonah Casebeer and Brian Luc and Paris Smaragdis",
note = "Funding Information: *These two authors contributed equally. This work was supported by NSF grant #1319708 Publisher Copyright: {\textcopyright} 2018 IEEE.; 16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 ; Conference date: 17-09-2018 Through 20-09-2018",
year = "2018",
month = nov,
day = "2",
doi = "10.1109/IWAENC.2018.8521280",
language = "English (US)",
series = "16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "496--500",
booktitle = "16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings",
address = "United States",
}