An Iterative Trajectory Regeneration Algorithm for Separating Mixed Speech Sources

Harmonicity and continuity are two important perceptual cues for separating mixed speech sources. This paper focuses on the separation of two speech sources with a single-microphone input. An iterative, least-squares (LS) based trajectory regeneration algorithm is proposed to estimate the magnitude spectrum of each source. Time-derivatives of the spectrum, or the dynamic spectral information, is used as a constraint in solving the resultant weighted normal equations. Each estimated spectral trajectory, as a result, exhibits similar temporal variations as the original source. Asymptotically, we also prove that the regenerated trajectory yields the same time variations as the given dynamic information. When cascaded with our previously proposed harmonic filtering algorithm to separate mixed voiced signals, the new trajectory regeneration is shown to be very effective to reduce mean squared errors by 82.2% and 69.5%, relatively, with ideal and approximated dynamic information, respectively