Remove Dirty Training Data of Piccolo

[Daniel-Chin]

Your paper mentioned some dirty training data of Piccolo that led to low test-time accuracy for Piccolo sounds. Do you think removing the problematic training data and training the model again will be a good idea?

The current model really learned to do two things: A) estimate pitch, B) if the instrument is classified as Piccolo, do a weird thing.

If we fix the dataset, the model won't have to learn a wrong thing anymore, which probably benefits its performance on other instruments too. Maybe then you will be able to decrease the model size without sacrificing accuracy?

[Daniel-Chin]

Since it is still SOTA (as of 2020), improving the model can be beneficial to many ;-)