
# 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/).  
[The paper](https://doi.org/10.1109/ICASSP49357.2023.10095569) was published in ICASSP 2023 ([arXiv](https://arxiv.org/abs/2211.16028)).

# 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      |

{"language": ["ja"], "license": "cc-by-nc-4.0", "tags": ["music", "speech", "audio", "audio-to-audio", "a cappella", "vocal ensemble"], "datasets": ["jaCappella"], "metrics": ["SI-SDR"]}
music speech audio audio-to-audio a cappella vocal ensemble ja dataset:jaCappella arxiv:2211.16028 license:cc-by-nc-4.0 region:us
TAGS
#music #speech #audio #audio-to-audio #a cappella #vocal ensemble #ja #dataset-jaCappella #arxiv-2211.16028 #license-cc-by-nc-4.0 #region-us 

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.


See the jaCappella dataset page.


For MRDLA, please cite the following paper.


Configuration
=============


Results (SI-SDR [dB]) on vocal ensemble separation
==================================================




TAGS
#music #speech #audio #audio-to-audio #a cappella #vocal ensemble #ja #dataset-jaCappella #arxiv-2211.16028 #license-cc-by-nc-4.0 #region-us 

TAGS
#music #speech #audio #audio-to-audio #a cappella #vocal ensemble #ja #dataset-jaCappella #arxiv-2211.16028 #license-cc-by-nc-4.0 #region-us 
