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.

Here the slides!

I hope this update makes this tutorial-like presentation more understandable to everyone!

Audio Transfer Learning with Scikit-learn and Tensorflow

We present a didactic toolkit to rapidly prototype audio classifiers with pre-trained Tensorlow models and Scikit-learn. We use pre-trained Tensorflow models as audio feature extractors, and Scikit-learn classifiers are employed to rapidly prototype competent audio classifiers that can be trained on a CPU.

Check it on Github!

This material was prepared for teaching Tensorflow, Scikit-learn, and deep learning in general. Besides, due to the simplicity of Scikit-learn, this toolkit can be employed to easily build proof-of-concept models with your own data.



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 🙂

Download the slides!