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This study investigates phase reconstruction for deep learning based monauraltalker-independent speaker separation in the short-time Fourier transform(STFT) domain. The key observation is that, for a mixture of two sources, withtheir magnitudes accurately estimated and under a geometric constraint, theabsolute phase difference between each source and the mixture can be uniquelydetermined; in addition, the source phases at each time-frequency (T-F) unitcan be narrowed down to only two candidates. To pick the right candidate, wepropose three algorithms based on iterative phase reconstruction, group delayestimation, and phase-difference sign prediction. State-of-the-art results areobtained on the publicly available wsj0-2mix and 3mix corpus.

Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective