Nabih Benchekroun

Bio: Nabih Benchekroun is an academic researcher. The author has contributed to research in topics: Signal processing & Blind signal separation. The author has an hindex of 1, co-authored 1 publications receiving 17 citations.

Blind separation of underwater acoustic signals

Abstract: In last two decades, many researchers have been involved in acoustic tomography applications. Recently, few algorithms have been dedicated to the passive acoustic tomography applications in a single input single output channel. Unfortunately, most of these algorithms can not be applied in a real situation when we have a Multi-Input Multi-Output channel. In this paper, we propose at first a realistic model of an underwater acoustic channel, then a general structure to separate acoustic signals crossing an underwater channel is proposed. Concerning ICA algorithms, many algorithms have been implemented and tested but only two algorithms give us good results. The latter algorithms minimize two different second order statistic criteria in the frequency domain. Finally, some simulations have been presented and discussed.

A survey of convolutive blind source separation methods

Abstract: In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks.

A source separation approach to enhancing marine mammal vocalizations

Abstract: A common problem in passive acoustic based marine mammal monitoring is the contamination of vocalizations by a noise source, such as a surface vessel. The conventional approach in improving the vocalization signal to noise ratio (SNR) is to suppress the unwanted noise sources by beamforming the measurements made using an array. In this paper, an alternative approach to multi-channel underwater signal enhancement is proposed. Specifically, a blind source separation algorithm that extracts the vocalization signal from two-channel noisy measurements is derived and implemented. The proposed algorithm uses a robust decorrelation criterion to separate the vocalization from background noise, and hence is suitable for low SNR measurements. To overcome the convergence limitations resulting from temporally correlated recordings, the supervised affine projection filter update rule is adapted to the unsupervised source separation framework. The proposed method is evaluated using real West Indian manatee (Trichechus m...

Single-Channel Signal Separation Using Spectral Basis Correlation with Sparse Nonnegative Tensor Factorization

Abstract: A novel approach for solving the single-channel signal separation is presented the proposed sparse nonnegative tensor factorization under the framework of maximum a posteriori probability and adaptively fine-tuned using the hierarchical Bayesian approach with a new mixing mixture model. The mixing mixture is an analogy of a stereo signal concept given by one real and the other virtual microphones. An “imitated-stereo” mixture model is thus developed by weighting and time-shifting the original single-channel mixture. This leads to an artificial mixing system of dual channels which gives rise to a new form of spectral basis correlation diversity of the sources. Underlying all factorization algorithms is the principal difficulty in estimating the adequate number of latent components for each signal. This paper addresses these issues by developing a framework for pruning unnecessary components and incorporating a modified multivariate rectified Gaussian prior information into the spectral basis features. The parameters of the imitated-stereo model are estimated via the proposed sparse nonnegative tensor factorization with Itakura–Saito divergence. In addition, the separability conditions of the proposed mixture model are derived and demonstrated that the proposed method can separate real-time captured mixtures. Experimental testing on real audio sources has been conducted to verify the capability of the proposed method.

Kernel and spectral methods for solving the permutation problem in frequency domain BSS

Abstract: In frequency domain blind source separation (FDBSS), separated frequency bin data in the same source must be grouped together before outputting the final result, which is the well-known permutation problem. Clustering techniques are broadly used in solving the permutation problem, however, some challenges still exist, for example, elongated datasets should be handled, and constraint from the background knowledge must be considered. Inspired by various successful applications of kernel and spectral clustering methods in machine learning and data mining community, we try to solve the permutation problem by these methods. In this paper, the weighted kernel k-means algorithm is modified according to the specific requirement of the permutation problem, and the spectral interpretation of the kernel approach is also investigated. In addition, we propose several kernel construction approaches to improving the permutation performance. Different experiments are carried out on a uniform platform, and show better performance of the proposed approach.

Blind Source Separation Based of Brain Computer Interface System: A review

Abstract: This study reviews the originality and development of the Brain Computer Interface (BCI) system and focus on the BCI system design based on Blind Source Separation (BSS) techniques. The study also provides the recent trends and discusses some of a new ideas for BSS techniques in BCI architecture, articles which discussing the BCI system development were analysed, types of the BCI systems and the recent BCI design were explored. Since 1970 when the research of BCI system began in the California Los Angeles University, the interest and the amount of research in BCI have been increased significantly; especially, when the BSS theory emerged in 1982 by a simple discussion between researchers. A lot of refereed journals and conference papers are reviewed and categorized to make this study in useful form. However, there are a few comprehensive reviews of BSS techniques in BCI literature. The review concludes with a brief discussion and expected future of the BCI.