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---
license: cc-by-nc-4.0
language:
- ja
tags:
- music
- speech
- audio
- audio-to-audio
- a cappella
- vocal ensemble
datasets:
- jaCappella
metrics:
- SI-SDR
---
# X-UMX 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/).
# Configuration
```yaml
data:
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
model:
bandwidth: 16000
bidirectional: true
hidden_size: 512
in_chan: 4096
nb_channels: 1
nhop: 1024
pretrained: null
spec_power: 1
window_length: 4096
optim:
lr: 0.001
lr_decay_gamma: 0.3
lr_decay_patience: 80
optimizer: adam
patience: 1000
weight_decay: 1.0e-05
training:
batch_size: 16
epochs: 1000
loss_combine_sources: true
loss_use_multidomain: true
mix_coef: 10.0
val_dur: 80.0
```
# Results (SI-SDR [dB]) on vocal ensemble separation
| Method | Lead vocal | Soprano | Alto | Tenor | Bass |Vocal percussion|
|:---------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|
| X-UMX | 7.5 | 10.7 | 13.5 | 10.2 | 9.1 | 21.0 | |