  Music source separation represents the task of extracting all the instruments
from a given song. Recent breakthroughs on this challenge have gravitated
around a single dataset, MUSDB, only limited to four instrument classes. Larger
datasets and more instruments are costly and time-consuming in collecting data
and training deep neural networks (DNNs). In this work, we propose a fast
method to evaluate the separability of instruments in any dataset without
training and tuning a DNN. This separability measure helps to select
appropriate samples for the efficient training of neural networks. Based on the
oracle principle with an ideal ratio mask, our approach is an excellent proxy
to estimate the separation performances of state-of-the-art deep learning
approaches such as TasNet or Open-Unmix. Our results contribute to revealing
two essential points for audio source separation: 1) the ideal ratio mask,
although light and straightforward, provides an accurate measure of the audio
separability performance of recent neural nets, and 2) new end-to-end learning
methods such as Tasnet, that operate directly on waveforms, are, in fact,
internally building a Time-Frequency (TF) representation, so that they
encounter the same limitations as the TF based-methods when separating audio
pattern overlapping in the TF plane.
