Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data

Zixing Zhang, Jing Han, Kun Qian, Christoph Janott, Yanan Guo, Bjorn Schuller

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data. To cope with this problem, we propose a novel data augmentation approach based on semi-supervised conditional generative adversarial networks (scGANs), which aims to automatically learn a mapping strategy from a random noise space to original data distribution. The proposed approach has the capability of well synthesizing 'realistic' high-dimensional data, while requiring no additional annotation process. To handle the mode collapse problem of GANs, we further introduce an ensemble strategy to enhance the diversity of the generated data. The systematic experiments conducted on a widely used Munich-Passau snore sound corpus demonstrate that the scGANs-based systems can remarkably outperform other classic data augmentation systems, and are also competitive to other recently reported systems for ASSC.

Original languageEnglish
Article number8678828
Pages (from-to)300-310
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
StatePublished - Jan 2020
Externally publishedYes


  • Snore sound classification
  • data augmentation
  • data synthesis
  • obstructive sleep apnea


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