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JNMR - Special Issue 2014 : Journal of New Music Research - Special Issue on Music and Machine Learning


When N/A
Where N/A
Submission Deadline Jul 1, 2013
Notification Due Oct 15, 2013
Final Version Due Dec 1, 2013

Call For Papers

Journal of New Music Research

Machine learning has permeated nearly every area of music informatics, driven by a renewed interest in computational musicology, the vast sources of music in digital audio formats, the steady increase in size and availability of symbolic corpora, and advances in semantic annotation of large audio collections.

To provide a collection of the highest quality contemporary work in machine learning and music, the Journal of New Music Research will host its second Special Issue on Music and Machine Learning. This Special Issue follows the successful JNMR 2011 Special Issue on Music and Machine Learning.

For the Special Issue on Music and Machine Learning we welcome submissions employing either symbolic or audio music data, and also encourage methods that bridge the symbolic/audio junction. Predictive as well as descriptive approaches to music data are equally encouraged. We invite contributions describing machine learning approaches to music processing including, but not limited to, the following topics:

Music Classification
Pattern and Structure Discovery
Music Generation and Algorithmic Composition
Computational Musicology
Music Knowledge Representation for Machine Learning
Music Clustering and Descriptive Rule Mining
Interactive Performance Systems
Cover Song Detection
Expressive Music Performance
Emotion and Mood
Psychoacoustics and Music Perception
Harmonic Analysis
Audio Chord Extraction
Playlist Analysis and Generation

Submissions must provide a thorough background on the music problem under study, a detailed description of the data set used, a sufficient description of the machine learning method employed, including algorithmic aspects where appropriate, and a strong evaluation of results. Manuscripts are submitted through the Journal of New Music Research ScholarOne site, not to the Guest Editors directly. On the "Manuscript Type" dropdown menu, authors must select "SI - Music and Machine Learning". Submissions must comply with the Journal's general guidelines for authors. In terms of paper length for the Special Issue, submissions should be roughly 6,000 words in length and formatted using latex options fullpage, 12pt, a4paper, doublespacing.

Manuscripts exceeding significantly the word count limit, or received after the deadline (below), will be returned without review to the authors and not considered for the Special Issue.

Important dates

Jan 2013 first call for papers
Jul 1, 2013 deadline for manuscript submission
Oct 15, 2013 responses to authors
Dec 1, 2013 camera ready papers due
early 2014 Special Issue in print

Guest Editors

General queries regarding the Special Issue and specifically the suitability of topics/research to the Special Issue should be directed to one of the Guest Editors:

Darrell Conklin
Universidad del País Vasco UPV/EHU
San Sebastián, Spain
Fundación Ikerbasque, Bilbao, Spain
darrell dot conklin at ehu dot es

José Manuel Iñesta
University of Alicante, Spain
inesta at dlsi dot ua dot es

Rafael Ramirez
Universitat Pompeu Fabra, Barcelona, Spain
rafael dot ramirez at upf dot edu

Related Resources

MNLP 2020   4th IEEE Conference on Machine Learning and Natural Language Processing
WSPML 2020   2020 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2020)
ICNMC 2020   7th International Conference on New Music Concepts
AICA 2020   O'Reilly AI Conference San Jose
IEEE CiSt 2020   6th IEEE Congress on Information Science and Technology
MAAIDL 2020   Springer Book 'Malware Analysis using Artificial Intelligence and Deep Learning'
SS DNAP IJCNN 2020   Special Session on Deep Neural Audio Processing at IEEE WCCI/IJCNN 2020
ICGI 2020   The 15th International Conference on Grammatical Inference
EvoMUSART 2020   9th International Conference on Artificial Intelligence in Music, Sound, Art and Design
JIST-KG 2020   Joint International Conference on Knowledge Graphs