@article {43,
	title = {A variable neighborhood search algorithm to generate piano fingerings for polyphonic sheet music},
	journal = {International Transactions in Operational Research, Special Issue on Variable Neighbourhood Search},
	volume = {24},
	year = {2017},
	month = {05/2017},
	pages = {509{\textendash}535},
	abstract = {A piano fingering indicates which finger should play each note in a piece. Such a guideline is very helpful for both amateur and experienced players in order to play a piece fluently. In this paper, we propose a variable neighborhood search algorithm to generate piano fingerings for complex polyphonic music, a frequently encountered case that was ignored in previous research. The algorithm takes into account the biomechanical properties of the pianist{\textquoteright}s hand in order to generate a fingering that is user-specific and as easy to play as possible. An extensive statistical analysis was carried out in order to tune the parameters of the algorithm and evaluate its performance. The results of computational experiments show that the algorithm generates good fingerings that are very similar to those published in sheet music books.},
	doi = {10.1111/itor.12211},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/itor.12211/full},
	author = {M. Balliauw and D. Herremans and Palhazi Cuervo, D. and K. S{\"o}rensen}
}
@article {29,
	title = {Classification and generation of composer-specific music using global feature models and variable neighborhood search},
	journal = {Computer Music Journal},
	volume = {39},
	year = {2015},
	pages = {91},
	chapter = {71},
	author = {D. Herremans and K. S{\"o}rensen and David Martens}
}
@inbook {42,
	title = {Composer Classification Models for Music-Theory Building},
	booktitle = {Computational Music Analysis},
	year = {2015},
	publisher = {Springer},
	organization = {Springer},
	issn = {978-3-319-25929-1 (Print) 978-3-319-25931-4 (Online)},
	doi = {0.1007/978-3-319-25931-4},
	url = {http://link.springer.com/book/10.1007/978-3-319-25931-4},
	author = {D. Herremans and David Martens and K. S{\"o}rensen and D. Meredith}
}
@inbook {35,
	title = {Generating Fingerings for Polyphonic Piano Music with a Tabu Search Algorithm},
	booktitle = {Mathematics and Computation in Music},
	series = {Lecture Notes in Computer Science},
	volume = {9110},
	year = {2015},
	pages = {149-160},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	keywords = {Combinatorial optimisation, Metaheuristics, OR in Music, Piano fingering, Tabu search},
	isbn = {978-3-319-20602-8},
	doi = {10.1007/978-3-319-20603-5_15},
	url = {http://dx.doi.org/10.1007/978-3-319-20603-5_15},
	author = {M. Balliauw and D. Herremans and Palhazi Cuervo, D. and K. S{\"o}rensen},
	editor = {Collins, Tom and Meredith, David and Volk, Anja}
}
@proceedings {30,
	title = {Generating music with an optimization algorithm using a Markov based objective function},
	year = {2015},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@article {31,
	title = {Generating structured music for bagana using quality metrics based on Markov models},
	journal = {Expert Systems With Applications},
	volume = {42 (21)},
	year = {2015},
	pages = {424{\textendash}7435},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@article {herremans2014dance,
	title = {Dance hit song prediction},
	journal = {Journal of New music Research},
	volume = {43},
	year = {2014},
	month = {10/09/14},
	pages = {302},
	publisher = {University of Antwerp, Faculty of Applied Economics},
	type = {Special Issue on Music and Machine Learning},
	chapter = {291},
	abstract = {<p>Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research we tackle this question by focussing on the dance hit song classification problem. A database of dance hit songs from 1985 until 2013 is built, including basic musical features, as well as more advanced features that capture a temporal aspect. A number of di erent classifiers are used to build and test dance hit prediction models. The resulting best model has a good performance when predicting whether a song is a top 10 dance hit versus a lower listed position.</p>},
	url = {http://ideas.repec.org/p/ant/wpaper/2014003.html},
	author = {D. Herremans and David Martens and K. S{\"o}rensen}
}
@proceedings {245,
	title = {First species counterpoint generation with VNS and vertical viewpoints},
	year = {2014},
	author = {D. Herremans and K. S{\"o}rensen and D. Conklin}
}
@article {243,
	title = {Generating structured music using quality metrics based on Markov models},
	number = {2014019},
	year = {2014},
	institution = {University of Antwerp},
	type = {Technical Report 2014:019- UA, Faculty of Applied Economics},
	address = {Antwerp},
	url = {https://ideas.repec.org/p/ant/wpaper/2014019.html},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@article {RePEc:ant:wpaper:2014001,
	title = {Looking into the minds of Bach, Haydn and Beethoven: Classification and generation of composer-specific music},
	number = {2014001},
	year = {2014},
	institution = {University of Antwerp, Faculty of Applied Economics},
	type = {Technical Report 2014:001- UA, Faculty of Applied Economics},
	abstract = {In this paper a number of musical features are extracted from a large music database, which are consequently used to build three composer classification models. The first two models, an if-then ruleset and a decision tree, result in an understanding of the style differences between Bach, Haydn and Beethoven. The third model, a logistic regression model, gives the probability that a piece is composed by a certain composer. This model is integrated in the objective function of a previously developed variable neighborhood search algorithm that can generate counterpoint. The result is a system that can generate an endless stream of counterpoint music with composer-specific characteristics that sounds pleasing to the ear. This system is implemented as an Android app called FuX that can be installed on any Android phone or tablet.},
	keywords = {Classification, Computer Aided Composition, Metaheuristics, Variable Neighborhood Search (VNS)},
	url = {http://ideas.repec.org/p/ant/wpaper/2014001.html},
	author = {D. Herremans and David Martens and K. S{\"o}rensen}
}
@conference {255,
	title = {Markov Based Quality Metrics For Generating Structured Music With Optimization Techniques},
	booktitle = {Digital Music Research Network (DMNR+9)},
	year = {2014},
	month = {16/12/2014},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@conference {244,
	title = {Sampling the extrema from statistical models of music with variable neighbourhood search},
	booktitle = {ICMC/SMC},
	year = {2014},
	publisher = {ICMC/SMC},
	organization = {ICMC/SMC},
	address = {Athens, Greece},
	author = {D. Herremans and K. S{\"o}rensen and D. Conklin}
}
@article {Herremans2013,
	title = {Composing Fifth Species Counterpoint Music With A Variable Neighborhood Search Algorithm},
	journal = {Expert Systems with Applications},
	volume = {40},
	year = {2013},
	month = {11/2013},
	chapter = {6427{\textendash}6437},
	issn = {0957-4174},
	doi = {10.1016/j.eswa.2013.05.071},
	url = {http://www.sciencedirect.com/science/article/pii/S0957417413003692},
	author = {D. Herremans and K. S{\"o}rensen}
}
@proceedings {173,
	title = {Dance Hit Song Science},
	year = {2013},
	edition = {6th},
	address = {Prague},
	abstract = {<p>With annual investments of several billions of dollars worldwide, record companies can benefit tremendously by gaining insight into what actually makes a hit song. This question is tackled in this research by focussing on the dance hit song problem prediction problem. A database of dance hit songs from 1985 until 2013 is built, including basic musical features, as well as more advanced features that capture a temporal aspect. Different classifiers are used to build and test dance hit prediction models. The resulting model has a good performance when predicting whether a song is a {\textquoteleft}{\textquoteleft}top 10\&$\#$39;\&$\#$39; dance hit versus a lower listed position.</p>},
	author = {D. Herremans and David Martens and K. S{\"o}rensen}
}
@proceedings {247,
	title = {First species counterpoint generation with VNS and vertical viewpoints},
	year = {2013},
	author = {D. Herremans and K. S{\"o}rensen and D. Conklin}
}
@conference {antor13,
	title = {FuX, an Android app that generates counterpoint},
	booktitle = {IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC)},
	year = {2013},
	month = {04/2013},
	pages = {48-55},
	doi = {10.1109/CICAC.2013.6595220},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=\&arnumber=6595220\&queryText\%3Dfux+an+android+app},
	author = {D. Herremans and K. S{\"o}rensen}
}
@proceedings {246,
	title = {Composing counterpoint musical scores with variable neighborhood search},
	year = {2012},
	author = {D. Herremans and K. S{\"o}rensen}
}
@article {RePEc:ant:wpaper:2012020,
	title = {Composing Fifth Species Counterpoint Music With Variable Neighborhood Search},
	number = {2012020},
	year = {2012},
	institution = {University of Antwerp, Faculty of Applied Economics},
	type = {Technical Report 2012:020- UA, Faculty of Applied Economics},
	abstract = {In this paper, a variable neighborhood search (VNS) algorithm is developed and analyzed that can generate fth species counterpoint fragments. The existing species counterpoint rules are quanti ed and form the basis of the objective function used by the algorithm. The VNS developed in this research is a local search metaheuristic that starts from a randomly generated fragment and gradually improves this solution by changing one or two notes at a time. An in-depth statistical analysis reveals the signi cance as well as the optimal settings of the parameters of the VNS. The algorithm has been implemented in a user-friendly software environment called Optimuse. Optimuse allows a user to input basic characteristics such as length, key and mode. Based on this input, a fth species counterpoint fragment is generated that can be edited and played back immediately. This work is the expansion of a previous paper by the authors in which rst species counterpoint music is composed by a similar VNS algorithm.},
	url = {http://ideas.repec.org/p/ant/wpaper/2012020.html},
	author = {D. Herremans and K. S{\"o}rensen}
}
@article {herremans2012,
	title = {Composing first species counterpoint musical scores with a variable neighbourhood search algorithm},
	journal = {Journal of Mathematics and the Arts},
	volume = {6},
	year = {2012},
	pages = {169 - 189},
	doi = {10.1080/17513472.2012.738554},
	author = {D. Herremans and K. S{\"o}rensen}
}
@article {99,
	title = {A Variable Neighborhood Search Algorithm for Composing First Species Counterpoint Musical Fragments},
	volume = {2011017},
	number = {Technical Report 2011:017- UA, Faculty of Applied Economics},
	year = {2011},
	month = {October},
	type = {Working paper, University of Antwerp, Faculty of Applied Economics},
	abstract = {In this paper a variable neighbourhood search (VNS) algorithm is developed that can generate musical fragments of arbitrary length consisting of a cantus firmus and a first species counterpoint melody. The objective function of the algorithm is based on a quantification of existing counterpoint rules. The VNS algorithm developed in this paper is a local search algorithm that starts from a randomly generated melody and improves it by changing one or two notes at a time. A thorough parametric analysis of the VNS reveals the significance of the algorithm{\textquoteright}s parameters on the quality of the composed fragment, as well as their optimal settings. The VNS algorithm has been implemented in a user-friendly software environment for composition, called Optimuse. Optimuse allows a user to specify a number of characteristics such as length, key, and mode. Based on this information, Optimuse {\textquotedblleft}composes{\textquotedblright} both a cantus firmus and a first species counterpoint melody. Alternatively, the user may specify a cantus firmus, and let Optimuse compose only an accompanying first species counterpoint melody.},
	issn = {Technical Report 2011:017- UA, Faculty of Applied Economics},
	url = {http://ideas.repec.org/p/ant/wpaper/2011017.html},
	author = {D. Herremans and K. S{\"o}rensen}
}
