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 🙂
Yesterday, I presented our work at the London Audio & Music AI Meetup and in a couple of weeks I’ll be also presenting at the Perth Machine Learning Group Meetup. The slides I’m using for those presentations and the recording of the video are now available online. Hopefully, they provide an additional perspective to our paper!