Connectionists: Deadline extension: special issue on Deep Learning for Music and Audio in Springer's NCA (IF: 2.505)

[Dorien Herremans]

*Special Issue on Deep Learning for Music and Audio*
in Springer's Neural Computing and Applications (Impact factor: 2.505)


*Extended submission deadline: *November 30th

*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://www.google.com/url?q=http%3A%2F%2Fdorienherremans.com%2Fdlm2017&sa=D&sntz=1&usg=AFQjCNEA9I6W1mbCRvphGJhEGF66A3qj-w>)
joint with IJCNN, this special issue aims to offer a venue for publishing
the latest state-of-the art in the field of DeepLearning 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:*
Prof. Dr. D. Herremans, Singapore University of Technology and Design
Prof. Dr. C.H. Chuan, University of North Florida

*Submission deadline:* November 30th

Please use the submission system of the journal for your submissions
and i*ndicate
the special issue during submission *at https://www.springer.com/journ
al/521/submission


Any *inquiries* can be directed at Prof. Dorien Herremans through
dorien_herremans [a] sutd dot edu [] com


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


-- 
Dorien Herremans, PhD
Assistant Professor
http://dorienherremans.com

Singapore University of Technology and Design
Information Technology and Design Pillar
Office 1.202-17
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