SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation
Year: 2025
Type: article
Abstract: Recent advancements in music source separation have significantly progressed, particularly in isolating vocals, drums, and bass elements from mixed tracks. These developments owe much to the creation and use of large-scale, multitrack datasets dedicated to these specific components. However, the challenge of extracting similarly sounding sources from orchestra recordings has not been extensively explored, largely due to a scarcity of comprehensive and clean (i.e bleed-free) multitrack datasets. In this paper, we introduce a novel multitrack dataset called SynthSOD, developed using a set of simulation techniques to create a realistic, musically motivated, and heterogeneous training set comprising different dynamics, natural tempo changes, styles, and conditions by employing high-quality digital libraries that define virtual instrument sounds for MIDI playback (a.k.a., soundfonts). Moreover, we demonstrate the application of a widely used baseline music separation model trained on our synthesized dataset w.r.t to the well-known EnsembleSet, and evaluate its performance under both synthetic and real-world conditions.
Authors: Jaime Garcia-Martinez, David Diaz-Guerra, Archontis Politis, Tuomas Virtanen, Julio J. Carabias-Orti, Pedro Vera-Candeas
Institutions: Universidad de Jaén, Tampere University, University of Tampere
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,795
OpenAlex ID: W4406347278