Blind Identification of Noisy Non-stationary Sources Using a Binary Mask

Abstract

In most functional blind source separation (BSS)
applications, the observations contain additive noise that limits
the performance of most existing BSS algorithms, especially in
the case where the noise is not modeled by a random process (e.g.,
electromagnetic power supply noise). In this paper, we describe
an algorithm where a new cost function is fed with a frequency
profile of the noise. In this way, the coefficients of the separating
matrix are identified without the bias introduced by the noise.
The proposed cost function is based on a frequency domain
binary mask and the coherence function. The binary mask selects
the frequencies where the signal-to-noise ratio (SNR) is relatively
high, while the coherence is minimized to obtain the inverse
system. Moreover, any frequency noise profile, whether given a
priori or estimated, can be applied to the binary mask to achieve
the identification of the inverse matrix. Computer simulations
show that the proposed algorithm exhibits better performance
under different SNR scenarios compared to methods developed
previously.