This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantane...

A robust approach that unifies independent component analysis (ICA) subspace feature selection in connection with the speaker verification (SV) is proposed. ICA subspace provides statistically independent basis that spans the input space of corrupted speech, then the selected independent components are applied to a vector quantizer (VQ) for SV purpose. The Euclidean distance in the feature spac...

background and objectives: evaluating the performance of clinical units is critical for effective managementof health settings. certain assessment of clinical variables for performance analysis is not always possible,calling for use of uncertainty theory. this study aimed to develop and evaluate an integrated independentcomponent analysis-fuzzy-data envelopment analysis approach to accurate the...

Myoelectric control has been an important area of investigation by researchers during the past forty years. An important role has been played by myoelecric signals in prosthetic control, since it targets the amputees who lost their body limb either in an accident or in the war. Remarkable advances were achieved with the number of movements to be classified with a high accuracy. This paper prese...

In this paper, we propose a method for composing templates of lung sound classification. First, we obtain a sequence of power spectra by FFT for each given lung sound and compute a small number of component spectra by ICA for each of the overlapping sets of tens of consecutive power spectra. Second, we put component spectra obtained from various lung sounds into a single set and conduct cluster...

It is well-known that the Independent Component Analysis (ICA) model is not identifiable when more than one source is Gaussian (Comon 1993). In this note, we show that the number of continuous degrees of freedom in the " identifiable quotient space " of ICA is max(0, G − 1), where G is the number of Gaussians in the sources. However, since the source distributions are unknown, G is unknown and ...

A novel contrast function is proposed to be used in fastICA algorithm for Blind Source Separation (BSS). Simulation results show that the proposed nonlinear function used to separate image mixtures, results in faster execution and good quality image separation. Peak Signal to Noise Ratio (PSNR), Improved Signal to Noise Ratio (ISNR), Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE)...