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Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners

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arxiv 1807.03046 v1 pith:KJ6FOE2G submitted 2018-07-09 cs.SD cs.IRcs.LGcs.MMeess.ASstat.ML

Emilia Gómez , Merlijn Blaauw , Jordi Bonada , Pritish Chandna , Helena Cuesta This is my paper · ORCID
classification cs.SDcs.IRcs.LGcs.MMeess.ASstat.ML
keywords achievementsadvanceschallengesdeepdiscussimpactlearninglisteners
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper summarizes some recent advances on a set of tasks related to the processing of singing using state-of-the-art deep learning techniques. We discuss their achievements in terms of accuracy and sound quality, and the current challenges, such as availability of data and computing resources. We also discuss the impact that these advances do and will have on listeners and singers when they are integrated in commercial applications.

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Cited by 1 Pith paper

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  1. MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI

    cs.SD 2025-07 conditional novelty 6.0 of 10

    MusGO is a community-refined framework with 13 openness categories, applied to 16 music-generative models to produce a public openness leaderboard.

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