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.
Category Archives: Slides
Slides & Video: Upsampling Artifacts in Neural Audio Synthesis
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!
Slides: Tutorial on Recurrent Neural Networks
Although I’m now a researcher at Dolby Laboratories, I’m still collaborating with some universities in Barcelona — where I’ll keep teaching deep learning for music and audio. In this context, and given the importance of the gradient vanishing/explode problem in deep neural networks, this week I’ll be teaching recurrent neural networks to the Master in Sound and Music Computing students of the Universitat Pompeu Fabra.
Slides update: Deep learning architectures for music audio classification: a personal (re)view
As part of my onboarding at Dolby, I had the pleasure to be working in San Francisco. In order to share my recent experiences with my colleagues, I have been updating these slides and I presented some of my recent work at Dolby and Adobe headquarters.
I hope this update makes this tutorial-like presentation more understandable to everyone!
Slides: Training neural audio classifiers with few data
During the last summer, I have been a research intern at Telefónica Research (Barcelona). The article “Training neural audio classifiers with few data” is the outcome of this short (but intense!) collaboration with Joan Serrà, where we explored how to train deep learning models with just 1, 2 or 10 audios per class. Check it out on arXiv, and reproduce our results running our code! These slides are the extended version of what I will be presenting next week in ICASSP! See you in Brighton 🙂