A few weeks ago Olga Slizovskaya and I were invited to give a talk to the Centre for Digital Music (C4DM) @ Queen Mary Universtity of London – one of the most renowned music technology research institutions in Europe, and possibly in the world. It’s been an honor, and a pleasure to share our thoughts (and some beers) with you!
The talk was centered in our recent work on music audio tagging, which is available on arXiv, where we study how non-trained (randomly weighted) convolutional neural networks perform as feature extractors for (music) audio classification tasks.
These lasts weeks we have been disseminating our recent work: “A Wavenet for Speech Denoising”. To this end, I gave two talks in the Bay Area of San Francisco: one at Dolby Laboratories and the other one at Pandora Radio — where I am currently doing an internship.
I was invited to give a talk to the Deep Learning for Speech and Language Winter Seminar @ UPC, Barcelona. Since UPC is the university where I did my undergaduate sudies, it was a great pleasure to give an introductory talk about how our community is using deep learning for approaching music technology problems.
Overall, the talk was centered in reviewing the state-of-the-art (1988-2016) in deep learning for music data processing in order to boost some discussion about current trends. Several key papers were chronologically listed and briefly described: pioneer papers using MLP , RNNs , LSTMs  and CNNs  for music data processing; and pioner papers using symbolic data , spectrograms  and waveforms  – among others.