This communication deals with the problem of blind separation of an instantaneous linear mixture of mutually un-correlated sources. A second order source separation technique exploiting the time coherence of the source signals is considered. Asymptotic performance analysis of the proposed method is performed. Several numerical simulations are presented to demonstrate the eeectiveness of the pro...

Renyi’s entropy can be used as a cost function for blind source separation (BSS). Previous works have emphasized the advantage of setting Renyi’s exponent to a value different from one in the context of BSS. In this paper, we focus on zero-order Renyi’s entropy minimization for the blind extraction of bounded sources (BEBS). We point out the advantage of choosing the extended zero-order Renyi’s...

A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors. In many real-world applications these limitations are too restrictive. We propose a method for underdetermined blind source separation of convolutive mixtures. The proposed framewor...

In the basic signal model of blind source separation (BSS), an unknown linear mixing process is assumed. While this ensures under mild conditions a suuciently unique solution, it is desirable to extend the problem to nonlinear mixtures. Unfortunately the nonlinear case is much more diicult to handle, and brings serious indeterminacies to the solutions in the general case. In this paper we propo...

We propose utilizing subband-based blind source separation (BSS) for convolutive mixtures of speech. This is motivated by the drawback of frequency-domain BSS, i.e., when a long frame with a fixed long frame-shift is used to cover reverberation, the number of samples in each frequency decreases and the separation performance is degraded. In subband BSS, (1) by using a moderate number of subband...

In this course project I investigated machine learning approaches on separating speech signals from background noise. Keywords—MFCC, SVM, noise separation, source separation, spectrogram

We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaura...

In this short note we highlight the fact that linear blind source separation can be formulated as a generalized eigenvalue decomposition under the assumptions of non-Gaussian, non-stationary, or non-white independent sources. The solution for the unmixing matrix is given by the generalized eigenvectors that simultaneously diagonalize the covariance matrix of the observations and an additional s...

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