Skip to Main Content
In this paper, the adaptive Least Mean Squares (LMS) algorithm is used to separate speaker-produced "information" from interferer-produced "noise" on the basis of the difference in power levels associated with the two phenomena. This method exploits the property of LMS that it rapidly adapts for the dominant excitation modes while simultaneously adapting very slowly for the weaker modes of excitation. This selective convergence property of LMS is next analyzed using an eigenvalue-eigenvector approach which easily displays the signal separation property. Lastly, computer simulations are presented which verify the analysis above for representative synthetic speech waveforms.