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Heterogeneous Separation Consistency Training for Adaptation of Unsupervised Speech Separation
[article]
2022
arXiv
pre-print
Recently, supervised speech separation has made great progress. However, limited by the nature of supervised training, most existing separation methods require ground-truth sources and are trained on synthetic datasets. This ground-truth reliance is problematic, because the ground-truth signals are usually unavailable in real conditions. Moreover, in many industry scenarios, the real acoustic characteristics deviate far from the ones in simulated datasets. Therefore, the performance usually
arXiv:2204.11032v3
fatcat:mcg4xhhsbfbvlgavrktfltkhsy