Transform-domain Wiener filter for speech periodicity enhancement

In this paper, we present a transform-domain Wiener filtering approach for enhancing speech periodicity. The enhancement is performed on the linear prediction residual signal. Two sequential lapped frequency transforms are applied to the residual in a pitch-synchronous manner. The residual signal is effectively represented by two separate sets of transform coefficients that correspond to the periodic and aperiodic components, respectively. A Wiener filter operating on the transform coefficients is developed to restore periodicity and reduce noise. Different filter parameters are designed for the transform coefficients of the periodic and aperiodic components. A template-driven method is used to estimate the filter parameters for the periodic component. For the aperiodic components, the filter parameters are computed based on a local SNR for effective noise reduction. Experimental results confirm that the harmonic structure of the signal can be effectively restored with the proposed approach.