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MorpheuS

Hybrid Machine Learning – Optimization techniques To Generate Structured Music Through Morphing And Fusion

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 MorpheuS project word cloud

Explore the words cloud of the MorpheuS project. It provides you a very rough idea of what is the project "MorpheuS" about.

critical    ideal    digital    ones    model    genetic    web    neighbourhood    billion    direct    levels    optimization    advertising    structure    generating    tackle    compositions    search    rule    moving    shown    powerful    made    techniques    music    website    constraints    machine    omax    optimisation    hierarchical    morph    operations    metrics    demonstrated    widest    continuator    state    lacks    first    arts    ing    framework    templates    situated    musical    structured    algorithm    proper    effect    area    2011    evaluation    projected    context    interactive    hybrid    learning    preserving    game    pieces    good    fuse    audience    vns    extensive    videos    varying    algorithms    learned    incorporate    note    effectiveness    coherence    33    styles    media    structural    promises    variety    combines    preliminary    expenditure    style    musician    automatic    researcher    direction    stock    deploys    generation    function    sounds    counterpoint    mimi    generate    outperforms   

Project "MorpheuS" data sheet

The following table provides information about the project.

Coordinator
QUEEN MARY UNIVERSITY OF LONDON 

Organization address
address: 327 MILE END ROAD
city: LONDON
postcode: E1 4NS
website: http://www.qmul.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website http://dorienherremans.com/morpheus
 Total cost 183˙454 €
 EC max contribution 183˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2015
 Duration (year-month-day) from 2015-06-01   to  2017-05-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    QUEEN MARY UNIVERSITY OF LONDON UK (LONDON) coordinator 183˙454.00

Map

 Project objective

State-of-the-art music generation systems (Continuator, OMax, Mimi) produce music that sounds good on a note-to-note level but lacks critical structure/direction necessary for long term coherence. To tackle this problem, we propose to generate compositions based on structural templates at varying hierarchical levels. Our novel approach deploys machine-learning methods in an optimization context to morph existing pieces into new ones and to fuse different styles.

We aim to develop a framework that combines machine learning techniques that learn style, with a powerful optimization method, the variable neighbourhood search (VNS) algorithm, for generating music. This approach allows the learned model to incorporate a wide variety of constraints, including those for preserving long term coherence and structure. It promises to effect a step-change in automatic music generation by moving the field in the new direction of generating structured music using hybrid machine learning-optimization techniques.

The applicant is an operations researcher-musician, ideal for this work. A first step combines her VNS music generation algorithm with machine learning methods to ensure proper style evaluation. In previous work, the applicant has shown that VNS outperforms genetic algorithms when generating counterpoint with a rule-based objective function. In a preliminary study, the applicant has demonstrated the effectiveness of using machine learning techniques as evaluation metrics for optimisation methods. The applicant has extensive web development experience; to reach the widest possible audience, the resulting system will be made available in an interactive website where users can morph and fuse musical pieces. This work is situated in the area of digital media, with a European consumer expenditure of over €33 billion in 2011, projected to increase. Music generation in digital music has direct applications in game music, interactive arts, and stock-music for advertising/videos.

 Publications

year authors and title journal last update
List of publications.
2016 Herremans D, Chew E
Music generation with structural constraints: an operations research approach
published pages: 37-39, ISSN: , DOI:
30th Annual Conference of the Belgian Operational Research (OR) Society (ORBEL30) 2019-07-24
2018 Herremans, D, Chuan, C.-H., Chew, E.
A Functional Taxonomy of Music Generation Systems
published pages: , ISSN: 0360-0300, DOI:
ACM Computing Surveys 2019-07-24
2017 Dorien Herremans, Elaine Chew
MorpheuS: generating structured music with constrained patterns and tension
published pages: 1-1, ISSN: 1949-3045, DOI: 10.1109/TAFFC.2017.2737984
IEEE Transactions on Affective Computing 2019-07-24
2016 Herremans D, Chew E
MorpheuS: Automatic music generation with recurrent pattern constraints and tension profiles
published pages: 282-285, ISSN: 2159-3450, DOI: 10.1109/TENCON.2016.7848007
IEEE TENCON 2019-07-24
2016 Agres K., Bigo L., Herremans D., Conklin D.
The Effect of Repetitive Structure on Enjoyment in Uplifting Trance Music
published pages: 280-282, ISSN: , DOI:
14th International Conference for Music Perception and Cognition (ICMPC) 2019-07-24
2017 Herremans D, Chuan CH
Modeling Musical Context with Word2vec
published pages: 11-18, ISSN: , DOI: 10.13140/RG.2.2.22227.99364/1
First International Workshop On Deep Learning and Music 2019-07-24
2017 Herremans D., Yang S., Chuan C.-H., Barthet M., Chew E..
IMMA-Emo: A Multimodal Interface for Visualising Score- and Audio-synchronised Emotion Annotations
published pages: , ISSN: , DOI:
Audio mostly 2019-07-24
2016 Herremans D, Chew E
MorpheuS: constraining structure in automatic music generation
published pages: 22, ISSN: , DOI:
Dagstuhl seminar on Computational Music Structure Analysis 6:2 2019-07-24
2017 Kat Agres, Dorien Herremans, Louis Bigo, Darrell Conklin
Harmonic Structure Predicts the Enjoyment of Uplifting Trance Music
published pages: 19999, ISSN: 1664-1078, DOI: 10.3389/fpsyg.2016.01999
Frontiers in Psychology 7 2019-07-24

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