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This paper presents a method for blind separation of convolutive mixtures of speech signals, based on the joint diagonalization of the time varying spectral matrices of the observation records and a novel technique to handle the problem of permutation ambiguity in the frequency domain. Simulations show that our method works well even for rather realistic mixtures in which the mixing filter has a quite long impulse response and strong echos.

Abstract. Cryptographic devices are vulnerable to the nowadays well known side channel leakage analysis. Secret data can be revealed by power analysis attacks such as Simple Power Analysis (SPA), Differential Power Analysis (DPA) and Correlation Power Analysis (CPA). First, we give an overview of DPA in mono-bit and multi-bit cases. Next, the existing multi-bit DPA methods are generalized into the proposed Partitioning Power Analysis (PPA) method. Finally, we focus on the CPA technique, showing that this attack is a case of PPA with special coefficients and a normalization factor. We also propose a method that allows us to improve the performance of CPA by restricting the normalization factor.

Conventional antenna array processing techniques are based on the use of second order statistics but rest on restrictive assumptions. Thus, when a priori information about the propagation or the geometry of the array are hardly available, the model can be generalized to a blind sources separation model, It supposes the statistical independence of the sources and their non-gaussianity. We focus in this paper on the generalization of the sources separation problem to convolutive mixtures of wide-band sources in the frequency domain.

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