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

TL;DR

This paper reviews recent deep learning advancements in singing processing, highlighting achievements in accuracy and sound quality, discussing ongoing challenges, and exploring the impact on singers and listeners in commercial applications.

Contribution

It provides a comprehensive overview of recent deep learning techniques applied to singing processing, emphasizing achievements, challenges, and potential impacts.

Findings

Deep learning has improved singing processing accuracy and sound quality.

Challenges include limited data availability and high computing resource requirements.

Advances are poised to significantly impact singing-related applications for performers and audiences.

Abstract

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.