Source separation using particle filters
Our goal is to study the statistical methods for source separation based on temporal and frequency specific features by using particle filtering. Particle filtering is an advanced state-space Bayesian estimation technique that supports non-Gaussian and nonlinear models along with time-varying noise, allowing for a more accurate model of the underlying system dynamics. We present a system that combines standard speech processing techniques in a novel method to separate two noisy speech sources. The system models the pitch and amplitude over time separately, and adopts particle filtering to reduce complexity by generating a discrete distribution that approximates well the desired continuous distribution. Preliminary results that demonstrate the separation of two noisy sources using this system are presented.