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On the Number of Channels in Multicomponent Nonstationary Noisy Signal Decomposition

If acquired using multiple sensors, non-stationary multicomponent signals can be decomposed into individual components by exploiting interdependences of signals from different channels. Earlier, we have proposed a decomposition approach being able to extract individual non-stationary signal components even in the challenging cases when their domains of support overlap in the time, frequency or joint time-frequency (TF) domains. The approach is based upon the eigenvalue analysis of the multichannel autocorrelation matrix and minimizations of concentration measures calculated using TF representations. In this paper, we investigate the influence of the number of sensors (channels) and external noise variance to the outcome of the decomposition process.