Research in deep learning for multi-speaker source separation has received a boost in the last years. However, most studies are restricted to mixtures of a specific number of speakers, called a specific scenario. While some works included experiments for different scenarios, research towards combining data of different scenarios or creating a single model for multiple scenarios have been very rare. In this work it is shown that data of a specific scenario is relevant for solving another scenario. Furthermore, it is concluded that a single model, trained on different scenarios is capable of matching performance of scenario specific models.
@article{arxiv.1808.08095,
title = {Multi-scenario deep learning for multi-speaker source separation},
author = {Jeroen Zegers and Hugo Van hamme},
journal= {arXiv preprint arXiv:1808.08095},
year = {2018}
}