@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}
}
@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}
}
@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}
}
@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}
}
