Benchmarks and leaderboards for sound demixing tasks
Roman Solovyev Alexander Stempkovskiy Tatiana Habruseva
Music demixing is the task of separating different tracks from the given
single audio signal into components, such as drums, bass, and vocals from the
rest of the accompaniment. Separation of sources is useful for a range of
areas, including entertainment and hearing aids. In this paper, we introduce
two new benchmarks for the sound source separation tasks and compare popular
models for sound demixing, as well as their ensembles, on these benchmarks. For
the models' assessments, we provide the leaderboard at
https://mvsep.com/quality_checker/, giving a comparison for a range of models.
The new benchmark datasets are available for download. We also develop a novel
approach for audio separation, based on the ensembling of different models that
are suited best for the particular stem. The proposed solution was evaluated in
the context of the Music Demixing Challenge 2023 and achieved top results in
different tracks of the challenge. The code and the approach are open-sourced
on GitHub.