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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:

https://media.nips.cc/Conferences/ICML2018/Styles/icml2018_style.tar.gz

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:

joint-music-machine-learning-workshop@googlegroups.com

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: