I am honored to be keynote speaker at a joint workshop on Machine Learning for Music that will take place this summer in Stockholm. I spent part of my PhD in KTH in Stockholm and it became one of my favourite places, so it is always great to visit. This is the call for papers! I encourage all of you to submit as it will be a fun event!
2nd Call-for-Papers for the 2018 Joint Workshop on Machine Learning for Music, a joint workshop program of IJCAI/ECAI, AAMAS, and ICML
July 14-15, 2018 (Tentative dates)
- Emilia Gómez, Joint Research Centre (European Commission) and Universitat Pompeu Fabra
- Anna Huang, Google
- Matt McVicar, Jukedeck
- Bob Sturm, Queen Mary University of London
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
- 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, and Pattern discovery
- Expressive performance modeling
2-page extended abstracts should be formatted according to the ICML template:
Word templates will not be provided. Only papers using the above template will be considered.
Papers should be submitted via email to the following address: firstname.lastname@example.org
Accepted papers will be published online.
- Abstract submission deadline: May 18, 2018
- Notifications: May 25, 2018
- Camera-ready deadline: July 6, 2018
- Workshop: July 14-15, 2018
Check the website (https://sites.google.com/site/faimmusic2018/ ) for more details, and regular updates on invited speakers.