Program
Program Summary
- 9:00 - 10:00am: Welcome and Keynote
- 10:00 - 11:00am: Poster presentations
- 11:00 - 12:20pm: Oral presentations
- 2:00 - 3:00pm: Oral presentations
- 3:00 - 4:30pm: Poster presentations
- 4:30 - 5:50pm: Oral presentations
- 6:00pm: Happy hour
9:00 - 10:00am: Keynote
From Listening to Watching, A Recommender Systems Perspective
Yves Raimond, Netflix
[Video]In this talk, I'll be discussing a few key differences between recommending music and recommending movies or TV shows, and how these differences can lead to vastly different designs, approaches, and algorithms to find the best possible recommendation for a user. On the other hand, I'll also discuss some common challenges and some of our recent research on these topics, such as better understanding the impact of a recommendation, enable better offline metrics, or optimizing for longer-term outcomes. Most importantly, I'll try to leave a lot of time for questions and discussions.
10:00 - 11:00am: Poster presentations
11:00 - 12:20pm: Oral presentations (20 min each)
Making Efficient use of Musical Annotations
Brian McFee (Invited Speaker)New York University[Video]Two-level Explanations in Music Emotion Recognition
Verena Haunschmid1, Shreyan Chowdhury1 and Gerhard Widmer1,2Johannes Kepler University Linz1 and Linz Institute of Technology (LIT)2[Video]Characterizing Musical Correlates of Large-Scale Discovery Behavior
Blair Kaneshiro (Invited Speaker)Stanford University[Video]NPR: Neural Personalised Ranking for Song Selection
Mark Levy, Matthias Mauch, Jan Van Balen, Bruno Di Giorgi and Dan CartoonApple[Video]2:00 - 3:00pm: Oral presentations (20 min each)
Personalization at Amazon Music
Kat Ellis (Invited Speaker)Amazon[Video]A Model-Driven Exploration of Accent Within the Amateur Singing Voice
Camille Noufi, Vidya Rangasayee, Sarah Ciresi, Jonathan Berger and Blair KaneshiroStanford University[Video]What’s Broken in Music Informatics Research? Three Uncomfortable Statements
Justin Salamon (Invited Speaker)Adobe Research[Video]3:00 - 4:30pm: Poster presentations
4:30 - 5:50pm: Oral presentations (20 min each)
User-curated shaping of expressive performances
Zhengshan Shi (Invited Speaker)Stanford University[Video]Interactive Neural Audio Synthesis
Lamtharn Hantrakul1, Adam Roberts1, Chenjie Gu1 and Jesse Engel2Google Brain1 and Google DeepMind2[Video]Visualizing and Understanding Self-attention based Music Tagging
Minz Won1, Sanghyuk Chun2, and Xavier Serra1Universitat Pompeu Fabra1, Naver Corp.2[Video]A CycleGAN for style transfer between drum & bass subgenres
Len Vande Veire, Tijl De Bie and Joni DambreGhent University[Video]Poster presentations
A Hybrid Approach to Audio-to-Score Alignment
Ruchit Agrawal and Simon DixonQueen Mary University of LondonThe MTG-Jamendo Dataset for Automatic Music Tagging
Dmitry Bogdanov, Minz Won, Philip Tovstogan, Alastair Porter and Xavier SerraUniversitat Pompeu FabraZero-shot Learning and Knowledge Transfer in Music Classification and Tagging
Jeong Choi1, Jongpil Lee1, Jiyoung Park2 and Juhan Nam1 Korea Advanced Institute of Science and Technology (KAIST)1 and NAVER Corp.2MsE-CNN: Multi-Scale Embedded CNN for Music Tagging
Nima Hamidi Ghalehjegh, Mohsen Vahidzadeh and Stephen BaekThe University of IowaA Comparison of Music Input Domains for Self-Supervised Feature Learning
Siddharth Gururani1, Alexander Lerch1 and Mason Bretan2Georgia Tech1 and Samsung Research America2Modeling Self-Repetition in Music Generation using Generative Adversarial Networks
Harsh Jhamtani1 and Taylor Berg-Kirkpatrick1,2Carnegie Mellon University1 and University of California San Diego2Representing Music Structure by Variational Attention
Junyan Jiang1,2, Gus Xia2 and Roger Dannenberg1 Carnegie Mellon University1 and New York University Shanghai2Modelling Interval Relations in Neural Music Language Models
Radha Manisha Kopparti and Tillman WeydeCity University of LondonExploiting repetitions in music with dynamic evaluation
Ben Krause, Emmanuel Kahembwe, Iain Murray and Steve RenalsUniversity of EdinburghRepresentation Learning of Music Using Artist, Album, and Track information
Jongpil Lee1, Jiyoung Park2 and Juhan Nam1 Korea Advanced Institute of Science and Technology (KAIST)1 and NAVER Corp.2Latent Space Regularization for Explicit Control of Musical Attributes
Kumar Ashis Pati and Alexander LerchGeorgia Institute of TechnologyScaling Up Music Tagging with Transfer Learning and Active Learning
Fedor Zhdanov1, Emanuele Coviello2 and Ben London2Amazon Research1 and Amazon Music2