ISMIR 2017 highlights

This has been my first ISMIR ever, and I am thrilled for being part of this amazing community. It was fun to put faces (and hight, and weight) to these names I respect so much!

All awarded papers were amazing, and these are definitely in my list of highlights:
  • Choi et al. – every time I re-read this paper I am more impressed about the efforts they put in assessing the generalization capabilities of deep learning models. This work defines a high evaluation standard for those working in deep auto-tagging models!
  • Bittner et al. proposes a fully-convolutional model for tracking f0 contours in polyphonic music. The article has a brilliant introduction drawing parallelisms between their proposed fully-convolutional architecture and previous traditional models – making clear that it is worth building bridges between deep learning works and previous signal processing literature.
  • Oramas et al. – deep learning enables to easily combine information from many sources, such as: audio, text or images. They do so by combining representations extracted from audio-spectrograms, word-embeddings and ImageNet-based features. Moreover, they released a new dataset: MuMu, with 147,295 songs belonging to 31,471 albums.
  • Jansson et al.‘s work proposes a U-net model for singing voice separation. It seems that adding connections between layers at the same hierarchical level in the encoder and decoder for reconstructing masked audio signals is a good idea since several papers already reported good results using this setup.

But there were many other inspiring papers.. Continue reading

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.

Three new arXiv articles

These last months have been very intense for us – and, as a result, three papers were recently uploaded to arXiv. Two of those have been accepted for presentation in ISMIR, and are the result of a collaboration with Rong – who is an amazing PhD student (also advised by Xavier) working on Jingju music:

The third paper was done in collaboration with Dario (an excellent master student!) who was interested in using deep learning models operating directly on the audio:

AI Grant and EUSIPCO paper accepted!

Our EUSIPCO 2017 paper got accepted! This paper was done in collaboration with Olga Slizovskaia, Rong Gong, Emilia Gómez and Xavier Serra. And it is entitled: “Timbre Analysis of Music Audio Signals with Convolutional Neural Networks”.

Paper blogpost with further details!

Link to the paper!

And I have been awarded with one of the AI Grants given by Nat Friedman for creating a dataset of sounds from Freesound and using it in my research. The AI grants are an initiative of Nat Friedman, Cofounder/CEO of Xamarin, to support open-source AI projects. The project I proposed is part of an initiative of the MTG to promote the use of Freesound.org for research. The goal is to create a large dataset of sounds, following the same principles as Imagenet – in order to make audio AI more accessible to everyone. The project will contribute in developing an infrastructure to organize a crowdsource tool to convert Freesound into a research dataset. The following video presents the aforementioned project: