In this paper, the speech separation task will be regarded as a convolutive mixture Blind Source Separation (BSS) problem. The Maximum Entropy (ME) algorithm, the Minimum Mutual Information (MMI) algorithm and the Maximum Likelihood (ML) algorithm are main approaches of the algorithms solving the BSS problem. The relationship of these three algorithms has been analyzed in this paper. Based on t...

In this paper, we address the problem of complex blind source separation (BSS), in particular, separation of nonstationary complex signals. It is known that, under certain conditions, complex BSS can be solved effectively by the so-called Strong Uncorrelating Transform (SUT), which simultaneously diagonalizes one Hermitian positive definite and one complex symmetric matrix. Our current work gen...

Independent component analysis (ICA) in the frequency domain is used for solving the problem of blind source separation (BSS). However, this method has some problems. For example, a general ICA algorithm cannot determine the permutation of signals which is important in the frequency domain ICA. In this paper, we propose an approach to the solution for a permutation problem. The idea is to effec...

This paper presented a kind of blind source separation (BSS) technology and applied it into the gearbox fault diagnosis through the blind mixing signal separation. The algorithm based on the natural gradient fixed step-length was used to calculate the statistical independent source signal estimate value, and successfully extracted the fault information according to the separation signal power s...

In different fields, the problem of Blind Source Separation (BSS) is known as a collection of linear combinations of unknown sources and worse the coefficients of the linear combinations that are unknown. The main problem is to estimate the matrix of the combination coefficients (mixing matrix) and to reconstruct the sources according to it. The quality of separation of sources from mixtures dr...

In Different Fields, The Problem Of Blind Source Separation (BSS) Is Known As A Collection Of Linear Combinations Of Unknown Sources And Worse The Coefficients Of The Linear Combinations That Are Unknown. The Main Problem Is To Estimate The Matrix Of The Combination Coefficients (Mixing Matrix) And To Reconstruct The Sources According To It. The Quality Of Separation Of Sources From Mixtures Dr...

Blind source separation (BSS) is one of the most important and established research topics in signal processing and many algorithms have been proposed based on different statistical properties of the source signals. For second-order statistics (SOS) based methods, canonical correlation analysis (CCA) has been proved to be an effective solution to the problem. In this work, the CCA approach is g...

This paper addresses blind source separation (BSS) problem when source signals have the temporal structure with nonlinear autocorrelation. Using the temporal characteristics of sources, we develop an objective function based on the nonlinear autocorrelation of sources. Maximizing the objective function, we propose a fixed-point source separation algorithm. Furthermore, we give some mathematical...

A new real-time two-stage blind source separation (BSS) for convolutive mixtures of speech is proposed, in which a SingleInput Multiple-Output (SIMO)-model-based ICA and binary mask processing are combined. SIMO-model-based ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. Thus, the sep...

ABSTRACT This paper presents a new method for Blind Source Separation (BSS) based on dual adaptive control, which allows successful separation of linear mixtures of independent source signals. The method reformulates a BSS problem to get a dual adaptive control problem. Then a Sigmoid MLP neural network is used to approximate the widesence-mixing matrix defined in the BSS problem. By solving th...