The global music market moves billions of dollars every year, most of which comes from streamingplatforms. In this paper, we present a model for predicting whether or not a song will appear in Spotify’s Top 50, a ranking of the 50 most popular songs in Spotify, which is one of today’s biggest streaming services. To make this prediction, we trained different classifiers with information from audio features from songs that appeared in this ranking between November 2018 and January 2019. When tested with data from June and July 2019, an SVM classifier with RBF kernel obtained accuracy, precision, and AUC above 80%
The construction of rankings consists of ordering retrieved results according to certain criteria. R...
This research aims to analyze the effect of feature selection on the accuracy of music popularity cl...
The Internet’s popularization has increased the amount of content produced and consumed on the web. ...
Using regression and classification machine learning algorithms, this study explores audio features ...
The growing use of predictive analysis can be seen in volatile industries such as the music industry...
Abstract. The possibility of a hit song prediction algorithm is both academically inter-esting and i...
In this thesis we wanted to solve three research questions. Firstly, we wanted to study the utilisa...
Abstract The emergence of streaming services, e.g., Spotify, has changed the way people listen to mu...
Hit song prediction, one of the emerging fields in music information retrieval (MIR), remains a cons...
Music streaming services like Spotify have changed the way consumers listen to music. Understanding ...
Digital music distribution is increasingly powered by automated mechanisms that continuously captur...
In the music market, superstars significantly dominate the market share, while predicting the top hi...
We present two novel datasets for hit song prediction (HSP): HSP-S and HSP-L. They are substantially...
Abstract. Over 87% of the streaming music is owned by four major record labels (Jones, 2018). Yet, t...
Digital music distribution is increasingly powered by automated mechanisms that continuously capture...
The construction of rankings consists of ordering retrieved results according to certain criteria. R...
This research aims to analyze the effect of feature selection on the accuracy of music popularity cl...
The Internet’s popularization has increased the amount of content produced and consumed on the web. ...
Using regression and classification machine learning algorithms, this study explores audio features ...
The growing use of predictive analysis can be seen in volatile industries such as the music industry...
Abstract. The possibility of a hit song prediction algorithm is both academically inter-esting and i...
In this thesis we wanted to solve three research questions. Firstly, we wanted to study the utilisa...
Abstract The emergence of streaming services, e.g., Spotify, has changed the way people listen to mu...
Hit song prediction, one of the emerging fields in music information retrieval (MIR), remains a cons...
Music streaming services like Spotify have changed the way consumers listen to music. Understanding ...
Digital music distribution is increasingly powered by automated mechanisms that continuously captur...
In the music market, superstars significantly dominate the market share, while predicting the top hi...
We present two novel datasets for hit song prediction (HSP): HSP-S and HSP-L. They are substantially...
Abstract. Over 87% of the streaming music is owned by four major record labels (Jones, 2018). Yet, t...
Digital music distribution is increasingly powered by automated mechanisms that continuously capture...
The construction of rankings consists of ordering retrieved results according to certain criteria. R...
This research aims to analyze the effect of feature selection on the accuracy of music popularity cl...
The Internet’s popularization has increased the amount of content produced and consumed on the web. ...