Sequential Monte Carlo simulation of dynamical models with slowly varying parameters: Application to audio

In this paper, we propose a slow time-varying partial correlation (STV-PARCOR) model for dynamical models with slowly varying parameters. Based on it, we develop an online joint parameter and signal estimation algorithm under the sequential Monte Carlo framework. It is believed that the proposed model and algorithm will improve on the standard Monte Carlo filter as it is known that the standard filter becomes highly degenerate for models with slowly varying parameters. The suggested algorithm is tested with real speech data and the results are compared with those generated using existing approaches.