jaCappella/MRDLA_jaCappella_VES_48k

MRDLA trained with the jaCappella corpus for vocal ensemble separation

This model was trained by Tomohiko Nakamura using the codebase).
It was trained on the vocal ensemble separation task of the jaCappella dataset.
The paper was published in ICASSP 2023 (arXiv).

License

See the jaCappella dataset page.

Citation

See the jaCappella dataset page.

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

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