Hi @sandesh_bharadwaj97, you can preprocess the datasets and try to employ an automatic method that can reduce the impact for the DNN training, e.g., for the bleeding you could look into methods like
Prätzlich, Thomas, et al. “Kernel additive modeling for interference reduction in multi-channel music recordings.” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015.
We have created the datasets such that it should not be possible to just “clean them up” by manually labeling or doing a simple signal processing based filtering:
- For the label swap, the label swap happens before mixing to four stems: E.g., if a song contains the five instruments “bass guitar”, “drumset”, “male singer”, “male background choir”, “synthesizer”) then we, e.g., put “male background choir” into “other” and mixed everything to the four stems “bass”, “drums”, “vocals” and “other” - now “other” contains a mixture of “vocals” and “other” and, hence, is a noisy target for training.
- For the bleeding, we used a set of random effects (reverb, gain, shifts) to create the bleeding and a simple filtering/summation should not be sufficient.
With these two datasets, we want to investigate how good models can become if they are trained on noisy training data (e.g., by modifying loss function, regularize the networks, …).
Please note that you can not use any model which is trained on other datasets than the one that you are allowed to use for leaderboard A or leaderboard B, respectively.
Kind regards
Stefan