Sparse Blind Source Separation (BSS) problems have recently received some attention. And some of them have been proposed for the unknown number of sources. However, they only consider the overdetermined case (i.e. with more sources than sensors). In the practical BSS, there are not prior assumptions on the number of sources. In this paper, we use cluster and Principal Component Analysis (PCA) t...

In this paper, we develop a multi-channel noise reduction algorithm based on blind source separation (BSS). In contrast to general BSS algorithms that attempt to recover all the signals, we explicitly estimate only the speech signal. By tracking the minimum of the spectral density of the microphone signals, noise-only segments are identified. The coefficients of the unmixing matrix that are nec...

Blind source separation (BSS) is a method for recovering a set of source signals from the observation of their mixtures without any prior knowledge about the mixing process. In BSS the definition of a source signal has an inherent indeterminacy; any linear transform of a source signal can also be considered a source signal. Due to this indeterminacy, there are an infinite number of valid separa...

Blind source separation (BSS) has been successfully applied to many fields such as communications and biomedical engineering. Its application for image encryption, however, remains largely unexplored. In this contribution, a novel BSS-based scheme is proposed for encrypting multiple images, in which the underdetermined BSS problem is fully exploited to achieve the image security. The necessary ...

In this paper, we investigate the separation and dereverberation performance of frequency domain Blind Source Separation (BSS) based on Independent Component Analysis (ICA) by measuring impulse responses of a system. Since ICA is a statistical method, i.e., it only attempts to make outputs independent, it is not easy to predict what is going on in a BSS system physically. We therefore investiga...

This paper describes a new blind source separation (BSS) method on microphone array using the subband independent component analysis (ICA) and beamforming. The proposed array system consists of the following three sections: (1) subband-ICA-based BSS section, (2) null beamforming section, and (3) integration of (1) and (2) based on the algorithm diversity. Using this technique, we can resolve th...

Despite several recent proposals to achieve Blind Source Separation (BSS) for realistic acoustic signal, separation performance is still not enough. In particular, when the length of impulse response is long, performance is highly limited. In this paper, we show it is useless to be constrained by the condition, P T , where T is the frame size of FFT and P is the length of room impulse response....

Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or ‘sources’ from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach but...

In this paper, we propose a noise-robust blind speech separation (BSS) method by using two microphones. We first use modulation domain real and imaginary spectral subtraction (MRISS) to enhance both magnitude and phase spectra of the speech mixture inputs. We then estimate the direction of arrivals (DOAs) of the speech sources and perform time-acoustic-modulation frequency masking to recover th...

Matrix factorizations and their extensions to tensor factorizations and decompositions have become prominent techniques for linear and multilinear blind source separation (BSS), especially multiway Independent Component Analysis (ICA), Nonnegative Matrix and Tensor Factorization (NMF/NTF), Smooth Component Analysis (SmoCA) and Sparse Component Analysis (SCA). Moreover, tensor decompositions hav...