  Music source separation performance has greatly improved in recent years with
the advent of approaches based on deep learning. Such methods typically require
large amounts of labelled training data, which in the case of music consist of
mixtures and corresponding instrument stems. However, stems are unavailable for
most commercial music, and only limited datasets have so far been released to
the public. It can thus be difficult to draw conclusions when comparing various
source separation methods, as the difference in performance may stem as much
from better data augmentation techniques or training tricks to alleviate the
limited availability of training data, as from intrinsically better model
architectures and objective functions. In this paper, we present the
synthesized Lakh dataset (Slakh) as a new tool for music source separation
research. Slakh consists of high-quality renderings of instrumental mixtures
and corresponding stems generated from the Lakh MIDI dataset (LMD) using
professional-grade sample-based virtual instruments. A first version,
Slakh2100, focuses on 2100 songs, resulting in 145 hours of mixtures. While not
fully comparable because it is purely instrumental, this dataset contains an
order of magnitude more data than MUSDB18, the {\it de facto} standard dataset
in the field. We show that Slakh can be used to effectively augment existing
datasets for musical instrument separation, while opening the door to a wide
array of data-intensive music signal analysis tasks.
