End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction
Table 1: SI-SDR (dB) performance on wsj0-2mix.
['Approaches', 'CSC', 'OSC']
We first present the results using sigmoidal activation. The chimera++ network obtains significantly better results than the individual models (11.2 dB vs. 10.4 dB and 10.0 dB SI-SDR). Performing end-to-end optimization using LWA improves the results to 11.6 dB from 11.2 dB, without requiring phase reconstruction post-processing. Further applying MISI post-processing for five iterations (MISI-5) on this model however does not lead to any improvements, likely because the mixture phase is used during training and the model compensates for it without expecting further processing. In contrast, training the network through MISI using LWA-MISI-5 pushes the performance to 12.2 dB.