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.
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!
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 🙂
A few weeks ago Olga Slizovskaya and I were invited to give a talk to the Centre for Digital Music (C4DM) @ Queen Mary Universtity of London – one of the most renowned music technology research institutions in Europe, and possibly in the world. It’s been an honor, and a pleasure to share our thoughts (and some beers) with you!
The talk was centered in our recent work on music audio tagging, which is available on arXiv, where we study how non-trained (randomly weighted) convolutional neural networks perform as feature extractors for (music) audio classification tasks.
I was invited to give a talk to the Deep Learning for Speech and Language Winter Seminar at the UPC in Barcelona. Since UPC is the university where I did my undergraduate studies, it was a great pleasure to give a talk there!
The talk was centered in my recent work on music audio tagging, which is available on arXiv and is summarized in these previous posts: deep learning architectures for music audio classification, and deep end-to-end learning for music audio tagging at Pandora.