Stefan Uhlich¹, Franck Giron¹, Michael Enenkl¹, Thomas Kemp¹, Naoya Takahashi², Yuki Mitsufuji²
¹Sony European Technology Center (EuTEC), Stuttgart, Germany
²Sony Corporation, Audio Technology Development Department, Tokyo, Japan
stefan.uhlich (at) eu.sony.com
- is_blind: no
- additional_training_data: no
- Code: not available
- Demos: not available
This submission blends two systems as described in [1]. The first system is
UHL1 and the second system is another set of four networks trained on
musdb with different random seeds. We linearly blend the raw outputs of the
two systems using
$$\hat{s}_i(n) = 0.5 * ( \hat{s}_{i, 1}(n) + \hat{s}_{i, 2}(n)$$
where $i = \{B, D, O, V\}$. From these estimates, we then compute the
power spectral densities and spatial covariance matrices of the multichannel
Wiener filter [2].
- [1] S. Uhlich, M. Porcu, F. Giron, M. Enenkl, T. Kemp, N. Takahashi and Y. Mitsufuji: Improving music source separation based on deep neural networks through data augmentation and network blending, Proc. ICASSP, 2017
- [2] A. A. Nugraha, A. Liutkus, and E. Vincent. "Multichannel music separation with deep neural networks." EUSIPCO, 2016.