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Music source separation in the time-frequency domain is commonly achieved byapplying a soft or binary mask to the magnitude component of (complex)spectrograms. The phase component is usually not estimated, but instead copiedfrom the mixture and applied to the magnitudes of the estimated isolatedsources. While this method has several practical advantages, it imposes anupper bound on the performance of the system, where the estimated isolatedsources inherently exhibit audible "phase artifacts". In this paper we addressthese shortcomings by directly estimating masks in the complex domain,extending recent work from the speech enhancement literature. The method isparticularly well suited for multi-instrument musical source separation sinceresidual phase artifacts are more pronounced for spectrally overlappinginstrument sources, a common scenario in music. We show that complex masksresult in better separation than masks that operate solely on the magnitudecomponent.

Learned complex masks for multi-instrument source separation