We consider the problem of convolutive blind source separation of stereo mixtures. This is often tackled using frequency-domain independent component analysis (FD-ICA), or time-frequency masking methods such as DUET. In these methods, the short-term Fourier transform (STFT) is used to transform the signal into the time-frequency domain. Instead of using a fixed time-frequency transform on each ...

This paper reports on numerical experiments on the ‘independent component analysis’ (ICA) of some nonGaussian stochastic processes. It is found that the orthonormal basis discovered by ICA are strikingly close to wavelet basis.

Uninterruptible power supply is the main motive of power utility companies that motivate them for identifying and locating the different types of faults as quickly as possible to protect the power system prevent complete power black outs using intelligent techniques. Thus, the present research work presents a novel method for detection of fault disturbances based on Wavelet Transform (WT) and I...

The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear...

The technique of independent component analysis (ICA) is applied for texture feature detection. In ICA an optimal transformation (with respect to the statistical structure of the image samples set) is discovered via blind signal processing. Any texture is considered as a mixture of independent sources (basic functions of detected transformation). Experimental comparison is documented on the com...

This work proposes a novel algorithm for independent component analysis (ICA) based on marginal density estimation. The proposed ICA algorithm aims to search for an effective demixing matrix as well as weighted Parzen window (WPW) representations for marginal densities of independent components so as to express a factorial joint density for high dimensional observations. Following the linear mi...

Independent Component Analysis (ICA) is a fundamental method for Blind Source Separation (BSS). Classical ICA takes data matrix input formed by vector data. This paper focuses on BSS with third-order tensor data, such as 2D images. Two approaches exist this problem. The first approach reshapes each into to apply classical ICA, structural information lost. second unfolds along different modes pe...

Topographic independent component analysis (TICA) is an interesting extension of the conventional ICA, which aims at finding a linear decomposition into approximately independent components with the dependence between two components is approximated by their proximity in the topographic representation. In this paper we apply the topographic ICA to gene expression time series data and compare it ...

Atrial Fibrillation is the most common sustained arrhythmia encountered by clinicians. Because of the invisible waveform of atrial fibrillation in atrial activation for human, it is necessary to develop an automatic diagnosis system. 12-Lead ECG now is available in hospital and is appropriate for using Independent Component Analysis to estimate the AA period. In this research, we also adopt a s...

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result detailed inference underlying regulatory networks, but the diversity experimental platforms and protocols introduces critical biases that hinder scalable analysis existing data. Here, we show structure E . coli transcriptome, as determined by Independent Component Analysis (ICA...