Active Source Estimation for Improved Source Separation

Recent work in blind source separation applied to anechoic mixtures of speech allows for reconstruction of sources that rarely overlap in a time-frequency representation. While the assumption that speech mixtures do not overlap significantly in time-frequency is reasonable, music mixtures rarely meet this constraint, requiring new approaches. We introduce a method that uses spatial cues from anechoic, stereo music recordings and assumptions regarding the structure of musical source signals to effectively separate mixtures of tonal music. We discuss existing techniques to create partial source signal estimates from regions of the mixture where source signals do not overlap significantly. We use these partial signals within a new demixing framework, in which we estimate harmonic masks for each source, allowing the determination of the number of active sources in important time-frequency frames of the mixture. We then propose a method for distributing energy from time-frequency frames of the mixture to multiple source signals. This allows dealing with mixtures that contain time-frequency frames in which multiple harmonic sources are active without requiring knowledge of source characteristics. *An abbreviated version of this paper was submitted on December 1st, 2006, to the EURASIP Journal on Applied Signal Processing.