FILTERING OF COLORED NOISE FOR SPEECH ENHANCEMENT AND CODING

Scalar and vector Kalman filters are implemented for filtering speech contaminated by additive white noise or colored noise and an iterative signal and parameter estimator which can be used for both noise types is presented. Particular emphasis is placed on the removal of colored noise, such as helicopter noise, by using state-of-the-art colored noise assumption Kalman filters. The results indicate that the colored noise Kalman fillers provide a significant gain in SNR, a visible improvement in the sound spectrogram, and an audible improvement in output speech quality, none of which are available with white noise assumption Kalman and Wiener filters. When the filter is employed as a prefilter for linear predictive coding, the coded output speech quality and intelligibility is greatly enhanced in comparison to direct coding of the noisy speech.