On Thursday 13th May from 17:00 – 19:00 (CET) I’ll be part of the workshop ‘Exploring connections between AI and Music’. The live-streamed event is free to watch, and marks the presentation of the AI and Music Festival and its first activity (more information here). To prepare for it, I reviewed previous works by music AI artists and researchers. This slide deck contains a summary of how I perceive the current music AI scene.
Our “Upsampling artifacts in neural audio synthesis” paper has now a GitHub page with code to experiment with its figures. These notebooks provide additional (interactive) material to further understand our findings.
How to extract audio objects with deep learning – without explicitly learning to extract those? In our ICASSP paper we propose multichannel-based learning, a technique closely related to self-supervised learning, differentiable digital signal processing, and universal sound separation.Continue reading
These are the papers we will be presenting at ICASSP 2021:
- Xiaoyu Liu, Jordi Pons. On permutation invariant training for speech source separation. [arxiv]
- Daniel Arteaga, Jordi Pons. Multichannel-based learning for audio object extraction. [arxiv]
- Jordi Pons, Santiago Pascual, Giulio Cengarle, Joan Serrà. Upsampling artifacts in neural audio synthesis. [arXiv, code]
- Christian J Steinmetz, Jordi Pons, Santiago Pascual, Joan Serrà. Automatic multitrack mixing with a differentiable mixing console of neural audio effects. [arXiv, demo]
- Joan Serrà, Jordi Pons, Santiago Pascual. SESQA: semi-supervised learning for speech quality assessment. [arXiv]
Infinite thanks to all my collaborators for the amazing work 🙂