Naoya Takahashi¹, Nabarun Goswami², Yuki Mitsufuji¹
¹Sony Corporation, Audio Technology Development Department, Tokyo, Japan
²Sony India Software Center, Bangalore, India
Naoya.Takahashi [at] sony.com
- is_blind: no
- additional_training_data: yes
- Code: not available
- Demos: not available
This submission uses a multi-scale multi-band DenseNets (MMDenseNet) as described in [1] with maximum scale 5, 3 bands, maximum growth rate 15. For each instrument, a MMDenseNet is trained to predict the target instrument amplitude from the mixture amplitude in the STFT domain (frame size: 4096, hop size: 1024). The raw output of each network is then combined by a multichannel Wiener filter as described in [2] where we estimate the power spectral densities and spatial covariance matrices from the DNN outputs.
We used the train part of musdb and an internal database with 804 songs as development set and randomly chose 48 songs as validation set, which is used to
perform early stopping and hyperparameter selection (growth rate,
number of layers).
- N. Takahashi and Y. Mitsufuji: Multi-scale multi-band DenseNets for audio source separation, Proc. WASPAA, 2017
- A. A. Nugraha, A. Liutkus, and E. Vincent. "Multichannel music separation with deep neural networks." EUSIPCO, 2016.