HackerPapers

arXiv:2301.12662cs.SD

SingSong: Generating musical accompaniments from singing

Chris Donahue, Antoine Caillon, Adam Roberts, Ethan Manilow, Philippe Esling, Andrea Agostinelli, Mauro Verzetti, Ian Simon, Olivier Pietquin, Neil Zeghidour, Jesse Engel

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create music featuring their own voice. To accomplish this, we build on recent developments in musical source separation and audio generation. Specifically, we apply a state-of-the-art source separation algorithm to a large corpus of music audio to produce aligned pairs of vocals and instrumental sources. Then, we adapt AudioLM (Borsos et al., 2022) -- a state-of-the-art approach for unconditional audio generation -- to be suitable for conditional "audio-to-audio" generation tasks, and train it on the source-separated (vocal, instrumental) pairs. In a pairwise comparison with the same vocal inputs, listeners expressed a significant preference for instrumentals generated by SingSong compared to those from a strong retrieval baseline. Sound examples at https://g.co/magenta/singsong

arXiv comments
Journal reference
DOI
Subjects
cs.SDSound cs.AIArtificial Intelligence cs.LGMachine Learning cs.MMMultimedia eess.ASAudio and Speech Processing

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Mar 2025 to Mar 2025.

Comments linking this paper, per year.

Discussed in comments1 thread

    • There's work in that area, it's sometimes called "accompaniment generation." https://arxiv.org/abs/2301.12662 https://fastsag.github.io/

      potatoman22View on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.