Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners
arXiv:1807.03046
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.
arXiv commentKeynote 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
Cited by in corpus (6)
- Content based singing voice source separation via strong conditioning using aligned phonemes
- Latent Space Explorations of Singing Voice Synthesis using DDSP
- Deep Autotuner: a Pitch Correcting Network for Singing Performances
- Deep Autotuner: A Data-Driven Approach to Natural-Sounding Pitch Correction for Singing Voice in Karaoke Performances
- An analysis of Iranian Music Intervals based on Pitch Histogram
- Searching Personal Collections