Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia that allow predictions with pre-trained deep learning models — and some of those are based on musicnn!Continue reading
I’m happy to share the highlights of my first paper with Dolby! We will be presenting this work at ICASSP 2020, in Barcelona.
Several improvements have been proposed to Conv-TasNet – that mostly focus on the separator, leaving its encoder/decoder as a (shallow) linear operator. We propose a (deep) non-linear variant of it, that is based on a deep stack of small filters. With this change, we can improve 0.6-0.9 dB SI-SNRi.
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.
Last November, I defended my doctoral thesis in front of Geoffroy Peeters, Perfecto Herrera, and Juhan Nam — and they honoured our work with a Cum Laude mention!
I attach the slides I used for the defense, and a link to my thesis.
And thanks again to all the jury, for your time and valuable feedback!
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.Continue reading