Slides: A Wavenet for Speech Denoising

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.

Here my slides.

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!

Here Dario’s slides deck.

Hopefully, checking our complementary views might help folks better understanding our work.

Slides: Deep learning for music data processing – a personal (re)view

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.

Download the slides!

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 [1], RNNs [2], LSTMs [3] and CNNs [4] for music data processing; and pioner papers using symbolic data [1], spectrograms [5] and waveforms [6] – among others.

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