Blind source separation using kurtosis, negentropy and maximum likelihood functions

Independent component analysis (ICA) is a thriving tool in separating blind sources from its determined or over-determined instantaneous mixture signals. FastICA is one of the successful algorithms in ICA. The objective of this paper is to examine various contrast functions using FastICA algorithm, and to find highly performed available contrast function for the application of speech signal analysis in noisy environments. The contrast function is a non-linear function used to measure the independence of the estimated sources from the observed mixture signals in FastICA algorithm. Kurtosis, negentropy and maximum likelihood functions are used as contrast functions in FastICA algorithm. The FastICA algorithm using these contrast functions is tested on the synthetic instantaneous mixtures and real time recorded mixture signals. We evaluate the performance of the contrast functions based on signal to distortion ratio, signal to artifact ratio, signal to interference ratio and computational complexity. The result shows the maximum likelihood function performs better than the other contrast functions in noisy environments.