  In the past, the field of drum source separation faced significant challenges
due to limited data availability, hindering the adoption of cutting-edge deep
learning methods that have found success in other related audio applications.
In this manuscript, we introduce StemGMD, a large-scale audio dataset of
isolated single-instrument drum stems. Each audio clip is synthesized from MIDI
recordings of expressive drums performances using ten real-sounding acoustic
drum kits. Totaling 1224 hours, StemGMD is the largest audio dataset of drums
to date and the first to comprise isolated audio clips for every instrument in
a canonical nine-piece drum kit. We leverage StemGMD to develop LarsNet, a
novel deep drum source separation model. Through a bank of dedicated U-Nets,
LarsNet can separate five stems from a stereo drum mixture faster than
real-time and is shown to significantly outperform state-of-the-art nonnegative
spectro-temporal factorization methods.
