Connectionists: Special Issue on Deep Learning for Music and Audio in Springer’s Neural Computing and Applications

[Dorien Herremans]

Special Issue on Deep Learning for Music and Audio

in Springer's Neural Computing and Applications
<https://link.springer.com/journal/521> (Impact factor: 1.492)





Submission deadline: November 17th



Description and covered topics



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. Following the
recent success of the First International Workshop on Deep Learning and
Music (DLM2017) <http://dorienherremans.com/dlm2017> joint with IJCNN, this
special issue aims to offer a venue for publishing the latest state-of-the
art in the field of Deep Learning for Music and Audio.



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.



The goal of this special issue is to provide a forum for advancing the
state-of-the-art in Deep Learning techniques in the field of Music and
Audio. High quality papers are welcomed, including but not limited to
topics listed below:



- Deep learning for feature extraction and semantic modeling for music and
audio

- Modeling hierarchical and long term music structures using deep learning

- Modeling ambiguity and preference in music

- Applications of deep networks for music and audio such as audio
transcription, voice separation, music recommendation and etc.

- Novel architectures designed for music and audio

- Software frameworks and tools for deep learning in music and audio





About the journal



Neural Computing & Applications is an international journal which publishes
original research and other information in the field of practical
applications of neural computing and related techniques such as genetic
algorithms, fuzzy logic and neuro-fuzzy systems.



All items relevant to building practical systems are within its scope,
including contributions in the area of applicable neural networks theory,
supervised and unsupervised learning methods, algorithms, architectures,
performance measures, applied statistics, software simulations, hardware
implementations, benchmarks, system engineering and integration and case
histories of innovative applications.



The Original Articles will be high-quality contributions, representing new
and significant research, developments or applications of practical use and
value.  They will be reviewed by at least two referees.



Guest editors:

Dr. D. Herremans, Queen Mary University of London

Prof. Dr. C.H. Chuan, University of North Florida



Submission deadline: November 17th



Please use the submission system of the journal for your submissions
and indicate
the special issue during submission at
https://www.springer.com/journal/521/submission



Any inquiries can be directed at Dr. Dorien Herremans through
dorien.herremans [a] gmail [] com





Join the Deep Learning for Music mailing list at
https://groups.google.com/forum/#!forum/icdlm








-- 
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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