Communication is the science of “reliable” transfer of information between two parties, in the sense that the information reaches the intended party with as few errors as possible. Modern wireless systems have many interfering sources that hinder reliable communication. The performance of receivers severely deteriorates in the presence of unknown or unaccounted interference. The goal of a recei...

Electroencephalogram (EEG) recordings are often contaminated with ocular and muscle artifacts. In this paper, the canonical correlation analysis (CCA) is used as blind source separation (BSS) technique (BSS-CCA) to decompose the artifact contaminated EEG into component signals. We combine the BSSCCA technique with wavelet filtering approach for minimizing both ocular and muscle artifacts simult...

We propose 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 frame-shift is used for a few seconds of speech, the number of samples in each frequency bin decreases and the separation performance is degraded. In our proposed subband BSS, (1) by using a moderate number of ...

This paper focuses on underdetermined blind source separation (BSS) of three speech signals mixed in a real environment from measurements provided by two sensors. Underdetermined BSS is a problem that has not yet been intensely studied and so far no satisfying solution has been obtained. The major issue encountered in previous work relates to the occurrence of distortion, which affects a separa...

This paper addresses the problem of blind source separation (BSS) of n independent sources from their m linear mixtures in the over-determined cases ( m n > ) with unknown and dynamically changing number of sources. The system architecture including an on-line source number estimator and an auto-adjust separation mechanism is considered based on the feed-forward neural network (FNN). To speed u...

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...

In this paper, we present a quasi-Newton (QN) algorithm for joint independent subspace analysis (JISA). JISA is a recently proposed generalization of independent vector analysis (IVA). JISA extends classical blind source separation (BSS) to jointly resolve several BSS problems by exploiting statistical dependence between latent sources across mixtures, as well as relaxing the assumption of stat...

Because it can be found in many applications, the Blind Separation of Sources (BSS) problem has raised an increasing interest. According to the BSS, one should estimate some unknown signals (named sources) using multisensor output signals (i.e. observed or mixing signals). For the Blind Separation of Sources (BSS) problem, many algorithms have been proposed in the last decade. Most of these alg...

In this paper, we present an efficient real-time implementation of a broadband algorithm for blind source separation (BSS) of convolutive mixtures. A recently introduced generic BSS framework based on a matrix formulation allows simultaneous exploitation of nonwhiteness and nonstationarity of the source signals using second-order statistics. We demonstrate here that this general scheme leads to...

For modal analysis of large structures, it is unpractical and expensive to use artificial excitation (e.g., shakers). However, engineering structures are most often subject to ambient loads (e.g., traffic and wind) that can be exploited for modal parameter estimation. One difficulty is that the actual loading conditions cannot generally be measured, and output-only measurements are available. T...