Abstract
In this paper, we address the problem of multimodal signal processing and present a manifold learning method to extract the common source of variability from multiple measurements. This method is based on alternating-diffusion and is particularly adapted to time series. We show that the common source of variability is extracted from multiple sensors as if it were the only source of variability, extracted by a standard manifold learning method from a single sensor, without the influence of the sensor-specific variables. In addition, we present application to sleep stage assessment. We demonstrate that, indeed, through alternating-diffusion, the sleep information hidden inside multimodal respiratory signals can be better captured compared to single-modal methods.
Original language | English |
---|---|
Title of host publication | 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 5758-5762 |
Number of pages | 5 |
ISBN (Electronic) | 9781467369978 |
DOIs | |
State | Published - 04 08 2015 |
Event | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia Duration: 19 04 2014 → 24 04 2014 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
---|---|
Volume | 2015-August |
ISSN (Print) | 1520-6149 |
Conference
Conference | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 |
---|---|
Country/Territory | Australia |
City | Brisbane |
Period | 19/04/14 → 24/04/14 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Common variable
- alternating-diffusion
- diffusion maps
- multimodal
- sleep