Interspeech2019: my highlights

This was my first Interspeech, and I was interested in understanding the field from the eyes of a “speech researcher” — instead of looking at it from the music/audio perspective, that is my field of expertise. After attending to Interspeech, I realized their sensibility for languages and how diverse is the community. The best of the conference? That one of the longest slides in the world was in town.

On Sunday, a local festivity was going on in Graz!
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musicnn: an open source, deep learning-based music tagger

The musicnn library (pronounced as “musician”) employs deep convolutional neural networks to automatically tag songs, and the models that are included achieve the best scores in public evaluation benchmarks. These state-of-the-art models have been released as an open-source library that can be easily installed and used. For example, you can use musicnn to tag this emblematic song from Muddy Waters — and it will predominantly tag it as blues!

 
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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.