Abstract:
This paper deals with the problem of speech enhancement when a corrupted speech signal with an additive noise is the only information available for processing. Kalman filtering is known as an effective speech enhancement technique in which speech signal is usually modeled as autoregressive (AR) process and represented in the state-space domain. In the above context, all the Kalman filter-based approaches proposed in the past operate in two steps: they first estimate the noise and the driving variances and parameters of the signal model, then estimate the speech signal. This paper presents an alternative solution that does not require the explicit estimation of noise and driving process variances. This deals with a new formulation of the steady-state Kalman filter gain estimation based on the use of external description of systems. Unlike the conventional approaches, no suboptimal Kalman filter is needed here.
