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TwoStep

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Description

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.

Results

Task n_blocks n_repeats batch size SI-SNRi(dB)
Paper sep_clean 8 4 - 16.10
Here sep_clean 8 4 - 15.23

References

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}
}

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