Linear minimum free energy estimation: a computationally efficient noise suppression spectral estimation algorithm

Simplified linear versions of the nonlinear minimum free energy (MFE) noise suppression algorithms are introduced. The LMFE algorithms result from the addition of a smoothness penalty function to the linear prediction cost function. The autoregressive (AR) parameters are chosen commensurate with a global minimum of the modified cost function. It is shown that this constrained optimization procedure reduces to a form of matrix regularization. The LMFE algorithms are calculable in real time as they require a negligible increase in complexity over the conventional autoregressive algorithms, including fast computational versions thereof. Linear MFE extensions are applicable to all conventional AR algorithms, and should in each case substantially increase the useful SNR range of these algorithms. Simulation results illustrating the single snapshot performance of these algorithms are given for both narrowband sources and combinations of narrowband and broadband sources subjected to various levels of Gaussian white noise