The 2018 Joint Workshop on Machine Learning for Music

The Federated Artificial Intelligence Meeting (FAIM)
A joint workshop program of ICML, IJCAI/ECAI, and AAMAS

Stockholm, Sweden
Saturday, July 14th, 2018

A Joint Meeting of the:
Machine Learning for Music Discovery Workshop (ICML)
Erik M. Schmidt, Oriol Nieto, Katherine M. Kinnaird, Fabien Gouyon, Gert Lanckriet

MML 2018: 11th International Workshop on Machine Learning and Music (ICML)
Rafael Ramirez Melendez, José M. Iñesta, Darrell Conklin

Keynote Speaker:
  • Emilia Gómez, Joint Research Centre (European Commission) and Universitat Pompeu Fabra
Invited Speakers:
  • Anna Huang, Google
  • Matt McVicar, Jukedeck
  • Jimena Royo-Letelier, Deezer
  • Bob Sturm, Queen Mary University of London

Call for Papers
The ever-increasing size and accessibility of vast music libraries has created a demand more than ever for machine learning systems that are capable of understanding and organizing this complex data. Further, the whole music ecosystem --from creation to consumption-- is being disrupted to its core by current developments in machine learning, and in particular recent advances in deep learning. The topics discussed in the workshop will span a variety of music generation and recommender systems challenges including cross-cultural recommendation, content-based audio processing and representation learning, automatic music tagging, synthesis, style-transfer, and evaluation.

We invite the research community, from both industry and academia, to submit 2-page extended abstracts on topics such as:
  • Music recommendation and discovery
  • AI-based music creation and machine creativity
  • Content-based and multimodal music recommender systems
  • Transfer learning and semi-supervised learning for music discovery
  • Audio and semantic content-based machine learning (e.g., genre, mood, style, rhythm)
  • Browsing and visualization of large music and listener datasets
  • Similarity metric learning
  • Learning to rank
  • Evaluation methodology
  • Deep learning applications for computational music research
  • Modeling hierarchical and long term music structures using deep learning 
  • Cognitive models of music
  • Modeling ambiguity and preference in music
  • Software frameworks and tools for deep learning in music 
  • Automatic classification of music (audio and MIDI)
  • Style-based interpreter recognition
  • Automatic composition and improvisation
  • Automatic score alignment
  • Polyphonic pitch detection
  • Chord extraction
  • Pattern discovery
  • Expressive performance modeling 

Abstracts should be formatted according to the ICML template:

Only papers using the above template will be considered.  Word templates will not be provided.  Review is single-blind.  A third page may be used for references only.

Papers should be submitted via email to the following address:

Accepted papers will be published online.

Important dates:
  • Abstract submission deadline: May 18, 2018 (11:59pm any time zone)
  • Notifications: May 25, 2018
  • Camera-ready deadline: July 6, 2018
  • Early regitration: from April 16, 2018 to May 31, 2018
  • Late registration: from June 1, 2018 to June 25, 2018

Sponsors and organization: