Multi-scenario deep learning for multi-speaker source separation
This addresses the limitation of existing methods being restricted to specific speaker counts, offering a more flexible solution for audio processing applications.
The paper tackled the problem of multi-speaker source separation across different numbers of speakers, showing that data from one scenario helps another and that a single model trained on multiple scenarios can match the performance of scenario-specific models.
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.