how to improve separation between sources? about deepconvsep HOT 2 CLOSED

I did a test according to the reaedme file.“Separate music into vocals, bass, drums, accompaniment in examples/dsd100/separate_dsd.py ”,the model is "model_dsd_fft_1024.pkl",But the results are not very good.Where did I go wrong?
The sample is"100 - Young Griffo - Pennies",and the result is "http://pan.baidu.com/s/1bpaOpxt".

from deepconvsep.

hi! well I suggest training with all the files in the dataset and doing some data augmentation. as any data driven approach, the more data you have, the better.
model_dsd_fft_1024.pkl is trained only on the dev set and it is used to replicate the results in the sisecmus challenge.
I am sorry I can't help you more with this. I am writing my thesis, supervising a few students and I have less time now.
I think there is little insight you will get by playing with the parameters. research community will gain little from this. rather you should invest your effort in proposing different architectures, improving robustness.

from deepconvsep.