readme: ---
  license: cc-by-nc-4.0
  language:
  - ja
  tags:
  - music
  - speech
  - audio
  - audio-to-audio
  - a cappella
  datasets:
  - jaCappella
  metrics:
  - SI-SDR
  ---
  
  # DPTNet trained with the jaCappella corpus for vocal ensemble separation
  
  This model was trained by Tomohiko Nakamura using [the codebase](https://github.com/TomohikoNakamura/asteroid_jaCappella)).
  It was trained on the vocal ensemble separation task of [the jaCappella dataset](https://tomohikonakamura.github.io/jaCappella_corpus/).
  
  # License
  See [the jaCappella dataset page](https://tomohikonakamura.github.io/jaCappella_corpus/).
  
  # Citation
  See [the jaCappella dataset page](https://tomohikonakamura.github.io/jaCappella_corpus/).
  
  # Configuration
  ```yaml
  data:
    num_workers: 12
    sample_rate: 48000
    samples_per_track: 13
    seed: 42
    seq_dur: 5.046
    source_augmentations:
    - gain
    sources:
    - vocal_percussion
    - bass
    - alto
    - tenor
    - soprano
    - lead_vocal
  filterbank:
    kernel_size: 32
    n_filters: 64
    stride: 16
  masknet:
    bidirectional: true
    chunk_size: 174
    dropout: 0
    ff_activation: relu
    ff_hid: 256
    hop_size: 128
    in_chan: 64
    mask_act: sigmoid
    n_repeats: 8
    n_src: 6
    norm_type: gLN
    out_chan: 64
  optim:
    lr: 0.005
    optimizer: adam
    weight_decay: 1.0e-05
  training:
    batch_size: 1
    early_stop: true
    epochs: 600
    gradient_clipping: 5
    half_lr: true
    loss_func: pit_sisdr
  ```
  # Results (SI-SDR [dB]) on vocal ensemble separation
  
  |     Method      |   Lead vocal   |    Soprano     |      Alto      |     Tenor      |      Bass      |Vocal percussion|
  |:---------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|
  |     DPTNet      |       8.9      |       8.5      |      11.9      |      14.9      |      19.7      |      21.9      |
  