@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 {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}
}
