readme: ---
  license: cc-by-nc-4.0
  language:
  - ja
  tags:
  - music
  - speech
  - audio
  - audio-to-audio
  - a cappella
  datasets:
  - jaCappella
  metrics:
  - SI-SDR
  ---
  
  # MRDLA 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/).
  
  For MRDLA, please cite the following paper.
  ```
  @article{TNakamura202104IEEEACMTASLP,
   author={Nakamura, Tomohiko and Kozuka, Shihori and Saruwatari, Hiroshi},
   journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
   title = {Time-domain audio source separation with neural networks based on multiresolution analysis},
   year=2021,
   doi={10.1109/TASLP.2021.3072496},
   month=apr,
   volume=29,
   pages={1687--1701},
  }
  ```
  
  # Configuration
  
  ```yaml
  data:
    in_memory: true
    num_workers: 12
    sample_rate: 48000
    samples_per_track: 13
    seed: 42
    seq_dur: 6.0
    source_augmentations:
    - gain
    sources:
    - vocal_percussion
    - bass
    - alto
    - tenor
    - soprano
    - lead_vocal
  loss_func:
    lambda_t: 10.0
    lambda_f: 1.0
    band: high
  model:
    C_dec: 64
    C_enc: 64
    C_mid: 768
    L: 12
    activation: GELU
    context: false
    f_dec: 21
    f_enc: 21
    input_length: 288000
    padding_type: reflect
    signal_ch: 1
    wavelet: haar
  optim:
    lr: 0.0001
    lr_decay_gamma: 0.3
    lr_decay_patience: 50
    optimizer: adam
    patience: 1000
    weight_decay: 0.0
  training:
    batch_size: 16
    epochs: 1000
  ```
  
  # Results (SI-SDR [dB]) on vocal ensemble separation
  
  |     Method      |   Lead vocal   |    Soprano     |      Alto      |     Tenor      |      Bass      |Vocal percussion|
  |:---------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|
  |     MRDLA       |       8.7      |      11.8      |      14.7      |      11.3      |      10.2      |      22.1      |
  