Authors:
A. K. Kattepur
1
;
F. Jin
2
and
F. Sattar
3
Affiliations:
1
INRIA, France
;
2
Nanyang Technological University, Singapore
;
3
University of Malaya, Malaysia
Keyword(s):
Single channel, Blind Source Separation (BSS), Respiratory Sound (RS), Non-negative Matrix Factorization (NMF).
Related
Ontology
Subjects/Areas/Topics:
Acoustic Signal Processing
;
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
Abstract:
In this paper, we attempt to extend single channel source separation techniques to the separation of respiratory sound (RS) and heart sounds (HS). This single channel recording is analyzed and shown to be a convolutive mixture model. After analyzing the reasons for failure of commonly used blind source separation algorithms, we evaluate the efficacy of non-negative matrix factorization (NMF) techniques for this application. Analysis on simulated single channel convolutive mixtures at various sensor positions has been performed. It indicates an average signal to interference ratio (SIR) improvement of greater than 10 dB for the optimal sensor locations. The corresponding range of received power has been also studied for reliable separation of RS and HS. Finally, the proposed model and the NMF separation performance are demostrated to work well on real RS recordings.