arXiv Machine Learning
Jul 28

Music-Source-Separation-Training (MSST): A Unified Framework for Training and Evaluating Music Demixing Models

arXiv:2607. 23395v1 Announce Type: cross Abstract: Music Source Separation (MSS), the task of recovering individual sound components (stems) from a polyphonic mixture, is central to applications ranging from karaoke and remixing to audio restoration and content production.

By Roman Solovyev, Ilya Kiselev, Alexander Stempkovskiy, Tatiana Gabruseva
arXiv AI
Jun 4

A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References

arXiv:2508. 14623v2 Announce Type: replace-cross Abstract: This paper examines the implications of using the Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) as both evaluation and training objective in supervised speech separation, when the training references contain noise, as is the case with the de facto benchmark WSJ0-2Mix.

By Simon Dahl Jepsen, Mads Gr{\ae}sb{\o}ll Christensen, Jesper Rindom Jensen