Toward Deep Drum Source Separation
Alessandro Ilic Mezza Riccardo Giampiccolo Alberto Bernardini Augusto Sarti
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.