Abstract We propose a single channel blind source separation algorithm for convolutively mixed linear frequency modulation (LFM) signals based on smoothed Wigner-Ville distribution (SWVD) time-frequency analysis, Canny edge detection, and Hough transform detection. First, the SWVD analysis diagram is obtained as an image LFM characteristics. Second, detection performed image. Then, used to dete...

It is exactly one hundred years ago when the first paper about development of polarography was published in 1922. Polarography considered a predecessor voltammetry, and this iconic electrochemical technique designed by Nobel laureate Jaroslav Heyrovsky. In short review, aim to highlight some most important achievements voltammetry so far. While hints are given theoretical works related various ...

One of the most important problems in Blind Source Separation of convolutive mixtures is the filtering ambiguity. One way to address this is to modify the separation algorithm to enforce some constraint. In this paper, according to our previous studies recently introduced in [El Rhabi et al., A penalized mutual information criterion for blind separation of convolutive mixtures, Signal Processin...

Room reverberation is a primary cause of failure in distant speech recognition (DSR) systems. In this study, we present a multichannel spectrum enhancement method for reverberant speech recognition, which is an extension of a single-channel dereverberation algorithm based on convolutive nonnegative matrix factorization (NMF). The generalization to a multichannel scenario is shown to be a specia...

This paper introduces a novel independent component analysis (ICA) approach to the separation of nonlinear convolutive mixtures. The proposed model is an extension of the well-known post nonlinear (PNL) mixing model and consists of the convolutive mixing of PNL mixtures. Theoretical proof of existence and uniqueness of the solution under proper assumptions is provided. Feedforward and recurrent...

We propose the Convex Hull Convolutive Non-negative Matrix Factorization (CH-CNMF) algorithm to learn temporal patterns in multivariate time-series data. The algorithm factors a data matrix into a basis tensor that contains temporal patterns and an activation matrix that indicates the time instants when the temporal patterns occurred in the data. Importantly, the temporal patterns correspond cl...

In this paper, we propose a modification to the correlation approach in convolutive blind source separation to achieve an improved robustness. An often used approach for separation of convolutive mixtures is the transformation to the time-frequency domain. This allows for the use of an instantaneous ICA algorithm independently in each frequency bin, which greatly reduces complexity. The drawbac...

A novel learning algorithm for blind source separation of postnonlinear convolutive mixtures with non-stationary sources is proposed in this paper. The proposed mixture model characterizes both convolutive mixture and post-nonlinear distortions of the sources. A novel iterative technique based on Maximum Likelihood (ML) approach is developed where the ExpectationMaximization (EM) algorithm is g...