Workshop on Deep Learning and Music

International Workshop on Deep Learning for Music
In conjunction with the 2017 International Joint Conference on Neural Networks
(IJCNN 2017))

14-19 May (1 day), Anchorage
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There has been tremendous interest in deep learning across many fields of study. Recently, these techniques have gained popularity in the field of music. Projects such as Magenta (Google's Brain Team's music generation project), Jukedeck and others testify to their potential.

While humans can rely on their intuitive understanding of musical patterns and the relationships between them, it remains a challenging task for computers to capture and quantify musical structures. Recently, researchers have attempted to use deep learning models to learn features and relationships that allow us to accomplish tasks in music transcription, audio feature extraction, emotion recognition, music recommendation, and automated music generation.

With this workshop we aim to advance the state-of-the-art in machine intelligence for music by bringing together researchers in the field of music and deep learning. This will enable us to critically review and discuss cutting-edge-research so as to identify grand challenges, effective methodologies, and potential new applications.

Papers and abstracts on the application of deep learning techniques on music are welcomed, including but not limited to:
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
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Invited speakers
Invited speakers include Dr. Oriol Nieto (Pandora), Prof. Dr. Douglas Eck (the Head of the Google Magenta team) (tentatively confirmed), and Dr. Kat Agres (A*STAR Institute of High Performance Computing).

Submissions of Papers
Papers of up to 5 pages using the following template are welcomed for a talk. Submissions will be evaluated according to their originality, technical soundness, and relevance to the workshop. The guidelines outlined in the workshop’s latex template should be followed. Contributions should be in PDF format and submitted to d [dot] with the subject line: [DLM17 paper submission]. Submissions do not need to be anonymised. Papers will be peer-reviewed and published in the proceedings of the workshop.

Submissions of Abstracts
Structured abstracts of max 2 pages can be submitted for a shorter talk. The abstracts should follow the same template as the papers and will be included in the proceedings. Abstracts should be in PDF format and submitted to with the subject line: [DLM17 abstract submission]. Abstracts will be peer-reviewed and included in the proceedings of the workshop.

Special Issue in Journal
Authors will be invited to submit a full paper version of their extended abstract for a special issue in an indexed journal. More details on this will be available soon.

Important Dates
Paper Submission Deadline: February 28th
Acceptance Notification: March 12th
Final versions due: March 23, 2017
Workshop Date: one day during conference May 14-19, 2017

Workshop registration will be handled by the main conference, please check IJCNN for more details.

Dorien Herremans (Queen Mary University of London, UK)
Ching-Hua Chuan (University of North-Florida, US)

Programme Committee

Dorien Herremans (Queen Mary University of London, UK)
Ching-Hua Chuan (University of North-Florida, US)
Louis Bigo (Université Lille 3, France)
Maarten Grachten (Austrian Research Institute for Artificial Intelligence, Austria)
Sebastian Stober (University of Potsdam, Germany)

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