Draft:Music Source Separation

  • Comment: Please remove all of the URLs to external sites under Example Approaches and Methodologies Employed Flat Out (talk) 06:04, 31 March 2025 (UTC)

Music Source Separation (MSS)[1] also known as Stem Separation, Demixing, Audio Source Separation or Unmixing[2] is a technique of separating one audio track into multiple audio tracks by targeting mixed material using Music Information Retrieval (MIR)[3] MSS is a branch of Signal Separation which was established in the mid-1990s as a technology to reconstruct one or more source signals from mixtures of them. The process is generally utilized by music professionals to separate existing recordings for the purposes of enhancing the balance of the mix, remixing or remastering. There are additional use cases where there is no multitrack or session files available of the sound recording so it becomes a necessity to rely on tools that can provide stem separation from a single audio file.

Audio Source Seperation, Signal Separatio
Music Source Separation

Initial audio source separation for commercial purposes resulted in a file that was non-destructively separated, so that the resulting files could be reconstructed and sound exactly like the original without introducing issues when all tracks were performed simultaneously.[4]

There are a wide variety of applications of the technology outside of music including teaching, forensics, speech separation, live sound cancelation, audio restoration, and VR/AR.[5][6]

  1. ^ "Papers with Code - Music Source Separation". paperswithcode.com. Retrieved 2025-03-27.
  2. ^ Petermann, Darius; Wichern, Gordon; Wang, Zhong-Qiu; Roux, Jonathan Le (May 2022). "The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks". ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 526–530. arXiv:2110.09958. doi:10.1109/ICASSP43922.2022.9746005. ISBN 978-1-6654-0540-9.
  3. ^ Qian, Jiale; Liu, Xinlu; Yu, Yi; Li, Wei (2023-01-12). "Stripe-Transformer: deep stripe feature learning for music source separation". EURASIP Journal on Audio, Speech, and Music Processing. 2023 (1): 2. doi:10.1186/s13636-022-00268-1. ISSN 1687-4722.
  4. ^ "Unmixing Layers". download.steinberg.net. Retrieved 2025-03-27.
  5. ^ Lab, Gaudio. "Introducing GSEP: The Backbone of Gaudio Studio's Audio Separation Technology". Remove Vocals and Extract Instruments | Gaudio Studio. Retrieved 2025-03-29.
  6. ^ "Method and Apparatus for Audio Source Separation | MIT Lincoln Laboratory". www.ll.mit.edu. Retrieved 2025-03-29.

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