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.
This last month, I have submitted my doctoral thesis entitled “Deep Neural Networks for Music and Audio Tagging”. I’ll be defending next November 15th, and I’m excited to announce that my jury will be conformed by Geoffroy Peeters, Perfecto Herrera, and Juhan Nam.
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!