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.
|Journal||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Publication status||Published - 27 Sep 2004|
|Event||Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada|
Duration: 17 May 2004 → 21 May 2004
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering