A two-step training procedure for source separation via a deep neural network. In the first step we learn a transform (and it’s inverse) to a latent space where masking-based separation performance using oracles is optimal. For the second step, we train a separation module that operates on the previously learned space.
| Task | n_blocks | n_repeats | batch size | SI-SNRi(dB) | |
|---|---|---|---|---|---|
| Paper | sep_clean | 8 | 4 | - | 16.10 |
| Here | sep_clean | 8 | 4 | - | 15.23 |
If you use this model, please cite the original work.
@article{tzinis2019two,
title={Two-Step Sound Source Separation: Training on Learned Latent Targets},
author={Tzinis, Efthymios and Venkataramani, Shrikant and Wang, Zhepei and Subakan, Cem and Smaragdis, Paris},
booktitle={ICASSP 2020-2020 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP)},
pages={},
year={2020},
organization={IEEE}
}and if you like using asteroid you can give us a star! ⭐