Multi-view networks for denoising of arbitrary numbers of channels

Jonah Casebeer, Brian Luc, Paris Smaragdis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages496-500
Number of pages5
ISBN (Electronic)9781538681510
DOIs
StatePublished - Nov 2 2018
Event16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Tokyo, Japan
Duration: Sep 17 2018Sep 20 2018

Publication series

Name16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings

Other

Other16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018
Country/TerritoryJapan
CityTokyo
Period9/17/189/20/18

Keywords

  • Deep learning
  • Denoising
  • Multichannel

ASJC Scopus subject areas

  • Signal Processing
  • Acoustics and Ultrasonics

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