The 2016 Signal Separation Evaluation Campaign

  • Antoine Liutkus
  • Fabian-Robert Stöter
  • Zafar Rafii
  • Daichi Kitamura
  • Bertrand Rivet
  • Nobutaka Ito
  • Nobutaka Ono
  • Julie Fontecave
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)

Abstract

In this paper, we report the results of the 2016 community-based Signal Separation Evaluation Campaign (SiSEC 2016). This edition comprises four tasks. Three focus on the separation of speech and music audio recordings, while one concerns biomedical signals. We summarize these tasks and the performance of the submitted systems, as well as provide a small discussion concerning future trends of SiSEC.

Keywords

Empirical Mode Decomposition Source Separation Nonnegative Matrix Factorization Deep Neural Network Ensemble Empirical Mode Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Antoine Liutkus
    • 1
  • Fabian-Robert Stöter
    • 2
  • Zafar Rafii
    • 3
  • Daichi Kitamura
    • 4
  • Bertrand Rivet
    • 5
  • Nobutaka Ito
    • 6
  • Nobutaka Ono
    • 7
  • Julie Fontecave
    • 8
  1. 1.Inria, Speech Processing TeamVillers-lès-NancyFrance
  2. 2.International Audio Laboratories ErlangenErlangenGermany
  3. 3.Gracenote, Applied ResearchEmeryvilleUSA
  4. 4.SOKENDAI (The Graduate University for Advanced Studies)KanagawaJapan
  5. 5.GIPSA-lab, CNRS, Univ. Grenoble Alpes, Grenoble INPGrenobleFrance
  6. 6.NTT Communication Science LaboratoriesNTT CorporationTokyoJapan
  7. 7.National Institute of InformaticsTokyoJapan
  8. 8.UJF-Grenoble 1/CNRS/TIMC-IMAG UMR 5525GrenobleFrance

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