In conjunction with the 2017 International Joint Conference on Neural
Networks
(IJCNN 2017))
14-19 May (1 day), Anchorage
More info <http://dorienherremans.com/dlm2017/>
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.
Topics
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
More info <http://dorienherremans.com/dlm2017/>
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
<http://dorienherremans.com/dlm2017/dlm17template.zip> should be followed.
Contributions should be in PDF format and submitted to
d.herremans@qmul.ac.uk 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 d.herremans@qmul.ac.uk 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 (such as Journal of New
Music Research or IEEE Transactions on Neural Networks (btc)). 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
Registration
Workshop registration will be handled by the main conference, please check
IJCNN for more details.
Organizers
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)
More info
<http://dorienherremans.com/dlm2017/>
<http://dorienherremans.com/dlm2017/>
--
Dorien Herremans, PhD
Marie-Curie Fellow
http://dorienherremans.com
Queen Mary University of London
School of Electronic Engineering and Computer Science
C4DM - Centre for Digital Music, London
Workshop on Deep Learning and Music <http://dorienherremans.com/dlm2017>,
May'17 Anchorage, Alaska.
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