Source number estimator using Gerschgorin disks
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
Separation capability of overcomplete ICA approaches
SIP'07 Proceedings of the 6th Conference on 6th WSEAS International Conference on Signal Processing - Volume 6
MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
Single channel audio source separation
WSEAS Transactions on Signal Processing
Polyphonic music separation based on the simplified energy splitter
WSEAS Transactions on Signal Processing
WSEAS Transactions on Signal Processing
FastICA algorithm for the separation of mixed images
WSEAS Transactions on Signal Processing
Nonlinear extension of inverse filters for decomposition of monotonic multi-component signals
WSEAS Transactions on Signal Processing
Analytical model of the CKC-based activity index variance
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
An adaptive algorithm for speech source separation in overcomplete cases using wavelet packets
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
Multichannel Blind Source Separation Using Convolution Kernel Compensation
IEEE Transactions on Signal Processing
Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria
IEEE Transactions on Signal Processing
Properties of activity index extended by higher-order moments
CSS'10 Proceedings of the 4th international conference on Circuits, systems and signals
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In this paper we introduce a novel technique that can be used as an indicator of the number of active signal sources in convolutive signal mixtures. The technique is designed so that the number of sources is estimated using only recorded signals and some marginal information, such as possible minimum and maximum triggering frequencies of sources, but no information on mixing matrix, other parameters of signal sources, etc. Our research is based on the convolution kernel compensation method (CKC), which is a blind source separation method. First, a correlation matrix of the recorded signals is estimated. Next, a measure of the global activity of the signal sources, called activity index, is introduced. The exact analytical model of the activity index variance was derived for the purpose of the estimation of the number of signal sources. Using the analytical model, the number of active signal sources can be estimated if some a priori marginal information is available. We evaluated these marginal parameter values in extensive simulations of compound signals. The number of sources, their lengths, signal-to-noise ratio, source triggerings, and the number of measurements were randomly combined in preselected ranges. By using the established marginal parameter values and increasing extension factors, the model of the activity index variance was deployed to estimate the number of signal sources. The estimation results using synthetic signal mixtures are very promising.