Program

We are very excited to present a program of 1 keynote, 4 invited speakers, and 15 accepted presenters!

Program Summary

Detailed Program

9:10 - 10am Keynote Presentation - Emilia Gómez 

Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners

Emilia Gómez1,2, Merlijn Blaauw2, Jordi Bonada2, Pritish Chandna2, Helena Cuesta2

Joint Research Centre (European Commission)1 and Universitat Pompeu Fabra2

This talk summarizes recent advancements on singing voice processing using deep-learning architectures. We discuss on the achievements of current models on a set of singing processing tasks (pitch estimation, source separation and synthesis) in terms of accuracy and sound quality. We then consider the current challenges related to data availability and computing resources. We finally discuss on the impact that these advancements do and will have in singers and listeners when integrated in commercial applications. 

11:00 - 12:20am Invited Presentations (20 min each)

Generating Structured Music Through Self-Attention

Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Andrew Dai, Matt Hoffman, Curtis Hawthorne, and Douglas Eck

Google Brain

A “Division of Labour” Approach to Generating Highly Structured and Varied Musical Melodies

Matt McVicar, Gabriele Medeot, Srikanth Cherla, Katerina Kosta, Samer Abdallah, Marco Selvi, Ed Newton-Rex, and Kevin Webster

Jukedeck

Artist Disambiguation in Large Catalogs: Metric Learning and Sampling Strategies

Jimena Royo-Letelier, Romain Hennequin, Viet-Anh Tran, Manuel Moussallam

Deezer

How Stuff Works: LSTM Model of Folk Music Transcriptions

Bob L. Sturm

KTH Royal Institute of Engineering

2:00 - 3:20 Accepted Oral Presentations 1 (20 min each)

Magenta.js: A JavaScript API for Augmenting Creativity with Deep Learning

Adam Roberts, Curtis Hawthorne, and Ian Simon

Google Brain

A Case Study of Deep-Learned Activations via Hand-Crafted Audio Features

Olga Slizovskaia1, Emilia Gómez1,2, and Gloria Haro1

Pompeu Fabra University1, European Commission - Joint Research Centre2

Attention as a Perspective for Learning Tempo-invariant Audio Queries

Matthias Dorfer1, Jan Hajic jr.2, and Gerhard Widmer1

Johannes Kepler University1 and Charles University2

A Hybrid of Deep Audio Feature and i-vector for Artist Recognition

Jiyoung Park1, Donghyun Kim1, Jongpil Lee2, Sangeun Kum2, and Juhan Nam2

NAVER Corp1 and KAIST2

4:30 - 5:10 Accepted Oral Presentations 2 (20 min each)

Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks

Cheng-Wei Wu1, Jen-Yu Liu1,2, Yi-Hsuan Yang2, and Jyh-Shing R. Jang1

National Taiwan University1 and Academia Sinica2

Improving DNN-based Music Source Separation using Phase Features

Joachim Muth1, Stefan Uhlich2, Nathanaël Perraudin3, Thomas Kemp2 Fabien Cardinaux2 Yuki Mitsufuji4

École Polytechnique Fédérale de Lausanne (EPFL)1

Sony European Technology Center2

Swiss Data Science Center3

Sony Corporation4

10:00 - 11:00am and 3:30 - 4:30pm Poster Presentations

Block-sparse RNNs Improve Modeling Experimental Evidence from Polyphonic Music Data Sets

Erik Ylipää Hellqvist

Swedish Institute of Computer Science

DeepDrum: An Adaptive Conditional Neural Network for Generating Drum Rhythms

Dimos Makris1, Maximos Kaliakatsos-Papakostas2, and Katia Lida Kermanidis1

Ionian University1 and Institute for Language and Speech Processing, R.C. "Athena''2

Evaluating Repetition Based Melody Prediction Over Different Context Lengths

Radha Manisha Kopparti and Tillman Weyde

City University of London

Explainable Musical Phrase Completion

Gregory W. Johnsen1, Ling Lin1, Lucia Yu2, Andrew Dempsey2, Vishwali Mhasawade2, Daniel Jaroslawicz1, and Iddo Drori1,2

Columbia University1 and New York University2

Harmonic Recomposition using Conditional Autoregressive Modeling

Kyle Kastner, Rithesh Kumar, Tim Cooijmans, and Aaron Courville

Université de Montréal

Incremental Learning for Recognition of Handwritten Mensural Notation

Luisa Micó, José M. Iñesta, and David Rizo

University of Alicante

Modeling Musical Onset Probabilities via Neural Distribution Learning

Jaesung Huh, Egil Martinsson, Adrian Kim, Jung-Woo Ha

NAVER Corp

Recommending Songs to Music Learners Based on Chord Content

Johan Pauwels, György Fazekas, and Mark B. Sandler

Queen Mary University of London

Towards Cover Song Detection Using Siamese Convolutional Neural Networks

Marko Stamenovic

Bose