My favourite ISMIR 2019 papers!

This year’s ISMIR was in Delft, the Netherlands. It seems like the community is starting to realise that the technologies developed by the ISMIR community can have an impact to our society – because they are starting to work! During the first days of the conference, many conversations were focusing on exploring ways to positively impact society. On the other side, technology-wise, we have seen (i) many people studying how to use musical domain knowledge to disentangle/structure/learn useful neural representations for many music applications, and (ii) many attention-based neural architectures.

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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.

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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!