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.
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.
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.
Our ISMIR 2019 tutorial on ‘Waveform-based music processing with deep learning’ got accepted! We will teach about music generation (Sander Dieleman), music classification (Jongpil Lee), and music source separation (myself)!
This is the first ICASSP I’m feeling that the conference has become a place where influential machine learning papers are presented. I’m happy to see that most of our community is not only employing ‘LSTMs for a new dataset‘, but are proposing novel and inspiring machine learning methods. Let’s see what happened in Brighton (UK)!
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 🙂