Name
Abstract | ||
|---|---|---|
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. Anumber of different classifiers are used to build and test dance hit prediction models. The resulting best model has a good performance when predictingwhether a song is a 'top 10' dance hit versus a lower listed position. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1080/09298215.2014.881888 | JOURNAL OF NEW MUSIC RESEARCH |
Keywords | DocType | Volume |
machine learning,databases,information retrieval,music analysis | Journal | 43 |
Issue | ISSN | Citations |
SP3 | 0929-8215 | 4 |
PageRank | References | Authors |
0.43 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dorien Herremans | 1 | 54 | 16.22 |
| David Martens | 2 | 66 | 9.52 |
| Kenneth Sörensen | 3 | 175 | 19.42 |