We propose the use of blind source separation (BSS) for separation of a machine signature from distorted measurements. Based on an analysis of the mixing processes relevant for machine source separation, we indicate that instantaneous mixing may hold in acoustic monitoring. We then present a bilinear forms-based approach to instantaneous source separation. For simulated acoustic mixing, we show...

In this contribution we review recent results obtained on blind source separation (BSS) and independent component analysis (ICA). In particular we show that maximi-sation of mutual information can lead to ICA, and we present new conditions on cross cumulants which guarantee that blind source separation has been performed.

Blind Source Separation (BSS) of speech sources has been subject of study during many years, and it still remains largely open and unsolved. Traditional BSS methods based on statistical properties of the signals, as well as recent methods such as time-frequency masking, normally need to know in advance the number of sources in the mixture to perform the separation. Additionally, there are many ...

This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, and performs permutation alignment. We confirmed experimentally that reasonably good separation was obtained with the present method witho...

This paper considers the application of Blind Source Separation (BSS) to electromyographic (EMG) data as an approach to isolating individual motor unit potentials (MUPs). BSS was applied both to needle EMG (nEMG) and surface EMG (sEMG), and experimental results were obtained that demonstrate the effectiveness of this approach. BSS is proposed as a technique to be incorporated into EMG methodolo...

Blind source separation is a statistical technique in which an observed mixed data is decomposed in source signals and mixing channel. In BSS normally no, or little knowledge about the mixing channel is available a priori. In this work a higher order differential Covariance based source separation technique is used to separate the physiological sources blindly from Monkey’s fMRI data. Proposed ...

In this work, we deal with the problem of nonlinear blind source separation (BSS). We propose a new method for BSS in overdetermined linear-quadratic (LQ) mixtures. By exploiting the assumption that the sources are sparse in a transformed domain, we define a framework for canceling the nonlinear part of the mixing process. After that, separation can be conducted by linear BSS algorithms. Experi...

Blind source separation (BSS) has been proposed as a method to analyze multi-channel electroencephalography (EEG) data. A basic issue in applying BSS algorithms is the validity of the independence assumption. In this paper we investigate whether EEG can be considered to be a linear combination of independent sources. Linear BSS can be obtained under the assumptions of non-Gaussian, non-stationa...

SUMMARY This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, estimates the direction of arrival of each source, and performs permutation alignment. We confirmed experimentally that reasonably...

Feed-Forward (FF-) and Feed-Back (FB-) structures have been proposed for Blind Source Separation (BSS). The FF-BSS systems have some degrees of freedom in the solution space, and signal distortion is likely to occur in convolutive mixtures. On the other hand, the FBBSS structure does not cause signal distortion. However, it requires a condition on the propagation delays in the mixing process. I...