Gas identification with microelectronic gas sensor in presence of drift using robust GMM

Sofiane Brahim-Belhouari, Amine Bermak, Philip Ching Ho Chan

Research output: Journal article publicationConference articleAcademic researchpeer-review

9 Citations (Scopus)


The pattern recognition problem for real life applications of gas identification is particularly challenging due to the small amount of data available and the temporal variability of the instrument mainly caused by drift. In this paper we present a gas identification approach based on class-conditional density estimation using Gaussian mixture models (GMM). A drift counteraction approach based on extracting robust feature using a simulated drift is proposed. The performance of the retrained GMM shows the effectiveness of the new approach in improving the classification performance in the presence of artificial drift.
Original languageEnglish
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publication statusPublished - 27 Sep 2004
Externally publishedYes
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: 17 May 200421 May 2004

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

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