Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jul 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2024Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audiocited by 10
- 2025MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AIcited by 7
- 2021Latent Space Explorations of Singing Voice Synthesis using DDSPcited by 3
- 2022A Computational Analysis of Pitch Drift in Unaccompanied Solo Singing using DBSCAN Clusteringcited by 0
- 2025MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI4 Jul 2025
- 2024Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio19 Jul 2024
- 2022A Computational Analysis of Pitch Drift in Unaccompanied Solo Singing using DBSCAN Clustering3 Apr 2022
- 2021Latent Space Explorations of Singing Voice Synthesis using DDSP12 Mar 2021