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.
But Dario (coauthor of the paper) also gave a talk in the Technical University of Munich, and I am excited to share his slides with you — since these have fantastic and very clarifying figures!
Hopefully, checking our complementary views might help folks better understanding our work.
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.