Toward Deep Drum Source Separation

arXiv:2312.09663v2 Announce Type: replace-cross
Abstract: 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 …

abstract adoption applications arxiv audio availability challenges cs.lg cs.sd data dataset deep learning edge eess.as found scale success type