Toward Deep Drum Source Separation

• URL: http://arxiv.org/abs/2312.09663v3
• Date: Mon, 20 May 2024 07:30:25 GMT
• Title: Toward Deep Drum Source Separation
• Authors: Alessandro Ilic Mezza, Riccardo Giampiccolo, Alberto Bernardini, Augusto Sarti,
• Abstract summary: We introduce StemGMD, a large-scale audio dataset of isolated single-instrument drum stems. Totaling 1224 hours, StemGMD is the largest audio dataset of drums to date. We leverage StemGMD to develop LarsNet, a novel deep drum source separation model.
• Score: 52.01259769265708
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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