Online Bayesian learning for dynamic source separation

Independent component analysis (ICA) is a popular approach for blind source separation when the source signals are stationary with fixed distribution functions. However, the source signals are nonstationary in real-world applications, e.g. the source signals may abruptly appear or disappear or even the number of sources may be changed by time. This study presents a nonstationary ICA for dynamic source separation through an online Bayesian learning procedure. In this procedure, we capture the evolved statistics of independent sources from the online observed signals. The mixing matrix is incrementally compensated at each frame and continuously propagated to the next frame. A variational Bayesian algorithm is established to estimate the nonstationary ICA parameters. The number of latent sources is automatically determined at each frame. In the experiments, the proposed method effectively recovers the source speech signals from different speakers in presence of different mixing scenarios.