학술논문

Blind Separation of More Sources than Sensors in Convolutive Mixtures
Document Type
Conference
Source
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on. 5:V-V 2006
Subject
Signal Processing and Analysis
Components, Circuits, Devices and Systems
Independent component analysis
Robustness
Acoustic sensors
Time frequency analysis
Humans
Auditory system
Speech
Blind source separation
Time domain analysis
Informatics
Language
ISSN
1520-6149
2379-190X
Abstract
We demonstrate that blind separation of more sources than sensors can be performed based solely on the second order statistics of the observed mixtures. This a generalization of well-known robust algorithms that are suited for equal number of sources and sensors. It is assumed that the sources are non-stationary and sparsely distributed in the time-frequency plane. The mixture model is convolutive, i.e. acoustic setups such as the cocktail party problem are contained. The limits of identifiability are determined in the framework of the PARAFAC model. In the experimental section, it is demonstrated that real room recordings of 3 speakers by 2 microphones can be separated using the method.