Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners
Sound
2018-07-10 v1 Information Retrieval
Machine Learning
Multimedia
Audio and Speech Processing
Machine Learning
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.
Cite
@article{arxiv.1807.03046,
title = {Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners},
author = {Emilia Gómez and Merlijn Blaauw and Jordi Bonada and Pritish Chandna and Helena Cuesta},
journal= {arXiv preprint arXiv:1807.03046},
year = {2018}
}
Comments
Keynote speech, 2018 Joint Workshop on Machine Learning for Music. The Federated Artificial Intelligence Meeting (FAIM), a joint workshop program of ICML, IJCAI/ECAI, and AAMAS