Dance Hit Song Prediction
This work addresses the music industry's need to identify hit songs for investment decisions, but it is incremental as it applies existing methods to a specific domain.
The researchers tackled the problem of predicting dance hit songs by building a database from 1985 to 2013 and using classifiers, resulting in a best model with good performance in distinguishing 'top 10' hits from lower positions.
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 different 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.