Blind Source Separation (BSS) is a statistical approach to separating individual signals from an observed mixture of a group of signals. BSS relies on only very weak assumptions on the signals and the mixing process (hence the “blind” descriptor) and this blindness enables the technique to be used in a wide variety of situations. Research in the field of Blind Source Separation has resulted in ...

In Blind Source Separation (BSS) processing, taking advantage of scaling factor indetermination and based on the floatingpoint representation, we propose a scaling technique applied to the separation matrix, to avoid the saturation or the weakness in the recovered source signals. This technique performs an Automatic Gain Control (AGC) in an on-line BSS environment. We demonstrate the effectiven...

This paper overviews a total solution for frequencydomain blind source separation (BSS) of convolutive mixtures of audio signals, especially speech. Frequency-domain BSS performs independent component analysis (ICA) in each frequency bin, and this is more efficient than time-domain BSS. We describe a sophisticated total solution for frequency-domain BSS, including permutation, scaling, circular...

Some research on sound source estimation by blind source separation (BSS) have been achieved and been satisfactory. On the aspect of sound source identification of rotating machine, some research is been achieved too. How to apply blind source separation to fault character identification and improve veracity of fault diagnosis is the research content of this paper. Based on the principle of min...

In this paper, we investigate the importance of the high frequencies in the problem of convolutive blind source separation (BSS) of speech signals. In particular, we focus on frequency domain blind source separation (FD-BSS), and show that when separation is performed in the low frequency bins only, the recovered signals are similar in quality to those extracted when all frequencies are taken i...

Blind Source Separation (BSS) refers to the process of recovering source signals from a given mixture of unknown source signals were in no prior information about source and mixing methodology is known. Independent Component Analysis (ICA) is widely used BSS technique which allows separation of source components from complex mixture of signals based on certain statistical assumptions. This pape...

Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localized in the time-frequency (t-f) domain. In this paper, we propose the use of STFD matrices for both whitening and recovery of the mixing matrix, which are two stages commo...

Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localizable in the time-frequency (t-f) domain. In this paper, we propose the use of STFD matrices for both whitening and mixing matrix recovery, two stages commonly required in...

Independent Component Analysis (ICA) and its mathematical ideas are presented for the problem of Blind Signal Separation (BSS) and multichannel blind deconvolution of independent source signals. BSS and ICA are emerging techniques that aspire to recover unobserved signals or sources from the observed mixtures. The aims of this paper are to review some new approaches and implement some new and u...