End-to-end training of time domain audio separation and recognition
Table 1: SDR and SI-SNR in dB for the min and max test (tt) datasets of the WSJ0-2mix database.
['Dataset', 'SDR', 'SI-SNR']
We use the best performing architecture according to [Luo2018_ConvTasNetSurpassing] and optimize it with the ADAM optimizer [Kingma2014_Adam]. In particular, following the hyper-parameter notations in the original paper, we set N=512, L=16, B=128, H=512, P=3, X=8 and R=3 with global layer normalization. For distributing over multiple GPUs, we split between the three repeating convolutional blocks. It can be seen that our implementation of the Conv-TasNet reaches a comparable performance on the min dataset when compared to the original paper. There is a slight degradation in performance on the max dataset caused by the mismatch of training and test data because the model never saw long single-speaker regions during training and learned to always output a speech signal on both outputs, while these regions are present in the max dataset.