This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
This m-file displays the time waveform for the Gaussian pulse function and the first and second derivatives of the Gaussian pulse function for a 0.5 nanosecond pulse width. Other values of pulse widths may be used by changing fs,t,t1. The program uses the actual first and second derivative equations for the Gaussian pulse waveforms. The first derivative is considered to be the monocycle or monopulse as discussed in most papers. The second derivative is the waveform generated from a dipole antenna used in a UWB system. Other information is contained in the file.
Random Number Generators（隨機數生成）包括gaussian random number generator、uniform random number generator、low-frequency hold generator、1/f noise generator等5種隨機信號生成的c源代碼
Hidden Markov Toolkit (HTK) 3.2.1
HTK is a toolkit for use in research into automatic speech recognition and has been developed by the Speech Vision Robotics Group at the Cambridge University Engineering Department (http://svr-www.eng.cam.ac.uk) and Entropic Ltd (http://www.entropic.com).
