Single Microphone Blind Audio Source Separation Using EM-Kalman Filter and Short+Long Term AR Modeling

Blind Source Separation (BSS) arises in a variety of elds in

speech processing such as speech enhancement, speakers diarization and

identi cation. Generally, methods for BSS consider several observations

of the same recording. Single microphone analysis is the worst underde-

termined case, but, it is also the more realistic one. In this article, the

autoregressive structure (short term prediction) and the periodic signa-

ture (long term prediction) of voiced speech signal are modeled and a

linear state space model with unknown parameters is derived. The Expec-

tation Maximization (EM) algorithm is used to estimate these unknown

parameters and therefore help source separation.