Speech enhancement by Kalman filtering with a particle filter-based preprocessor

To reduce nonstationary noise in real environments, we propose to use a particle filter as a preprocessor of Kalman filtering. From noisy input speech signals, the autoregressive (AR) model parameters are estimated by using a particle filter. Clean speech signal is estimated by a Kalman filter configured with the estimated parameters. Experimental results show that when speech signal is corrupted by babble noise, the proposed algorithm improves the output SNR by 1.5 dB.