Special Issue on Deep Learning for Music and Audio
in Springer's Neural Computing and Applications (Impact factor: 2.50)

Submission deadline: December 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) 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.

Featured contributions fall into several categories: Original Articles, Review Articles, Forum Presentations, Book Reviews, Announcements and NCAF News.

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. The Forum Presentations will be summaries of oral presentations made at quarterly meetings of the Natural Computing Applications Forum which will generally be reviewed by one referee.

Guest editors:
Prof. 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 Prof. Dorien Herremans through dorien _ herremans [a] suted [] edu [] sg

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