Sequential Blind Source Extraction For Quasi-Periodic Signals With Time-Varying Period

Abstract

International audienceA novel second-order-statistics-based sequential blind extraction algorithm for blind extraction of quasi-periodic signals, with time-varying period, is introduced in this paper. Source extraction is performed by sequentially converging to a solution that effectively diagonalizes autocorrelation matrices at lags corresponding to the time-varying period, which thereby explicitly exploits a key statistical nonstationary characteristic of the desired source. The algorithm is shown to have fast convergence and yields significant improvement in signal-to-interference ratio as compared to when the algorithm assumes a fixed period. The algorithm is further evaluated on the problem of separation of a heart sound signal from real-world lung sound recordings. Separation results confirm the utility of the introduced approach, and listening tests are employed to further corroborate the results

Kurtosis Based Techniques for Signal Extraction

“…Non-Gaussianity is actually of paramount importance in BSS estimation [1,4]. Without non-Gaussianity, the BSS estimation is not possible at all [4,5]. To extract one source signal exclusively, it is sufficient to maximize/minimize the non-Gaussianity of y(k) in (1).…”

“…The major drawback of conventional BSS technique is that we must extract all independent components from their mixtures. In many applications especially in biomedical signal processing, someone is only interested in one or a few original sources [4,5]. For example, in EEG or MEG we can obtain more than 64 sensor signals but only a few signals (e.g.…”

ICA-Based Fetal Monitoring

“…For example, SOBI (Belouchrani et al, 1997) seeks the matrix Q as the joint diagonalizer of a set of covariance matrix C v (τ i ) for a preselected set of time-lags {τ 1 , τ 2 , τ 3 ,...}. Some steps to investigate the optimal choice of {τ 1 , τ 2 , τ 3 ,...} in context of the fECG extraction problem have been done by (Tsalaile et al, 2009). …”

Hybrid Source Extraction Techniques for Periodic Signals

“…Until now, a few BSE algorithms have been proposed for extraction of a specific signal by using some priori information, such as non-Gaussianity [5], smoothness or linear predictability [6,7], coding complexity [8,9], etc. For instance, Lu and Rajapakse [7] introduce a method on the basis of higher-order statistics.…”