1. The authors should make clear the distinction of when the proposed method is trained using only weak supervision and when it is semi-supervised trained. For instance, in Table 1, I think the proposed framework row refers to the semi-supervised version of the method, thus the authors should rename the column to ‘Fully supervised’ from ‘Supervised’. Maybe a better idea is to specify the data used to train ALL the parts of each model and have two big columns ‘Mixture training data’ and ‘Single source data’ which will make it much more prevalent of what is which.
2. Building upon my previous argument, I think that when one is using these large pre-trained networks on single-source data like CLAP, the underlying method becomes supervised in a sense, or to put it more specifically supervised with unpaired data. The authors should clearly explain these differences throughout the manuscript.
3. I think the authors should include stronger text-based sound separation baselines like the model and ideally the training method that uses heterogeneous conditions to train the separation model in [A] which has already shown to outperform LASS-Net (Liu et. al 2022) which is almost always the best performing baseline in this paper.
I would be more than happy to increase my score if all the above weaknesses are addressed by the authors.
[A] Tzinis, E., Wichern, G., Smaragdis, P. and Le Roux, J., 2023, June. Optimal Condition Training for Target Source Separation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
1. The authors should make clear the distinction of when the proposed method is trained using only weak supervision and when it is semi-supervised trained. For instance, in Table 1, I think the proposed framework row refers to the semi-supervised version of the method, thus the authors should rename the column to ‘Fully supervised’ from ‘Supervised’. Maybe a better idea is to specify the data used to train ALL the parts of each model and have two big columns ‘Mixture training data’ and ‘Single source data’ which will make it much more prevalent of what is which.