Abstract
In terms of determining the success of a musical artist's song, there is a positive correlation of radio play success and music sales success. Therefore, being able to forecast the future plays of a song on the radio can serve as powerful risk management and product portfolio management tools for record labels and other stakeholders of a song. This research strives to predict the remaining product life cycle of a song on the radio after it has been played for one or two months. The best results were achieved using a k-d tree to calculate the songs the most similar to the test songs and use a Random Forest model to forecast radio plays. Accuracy of 82.78% and 83.44% was achieved for the two time periods, respectively. This explorative research leads to over 4500 test metrics to find the best combination of models and pre-processing techniques. Other algorithms tested were KNN, MLP, and CNN. The features only consist of daily radio plays and use no musical features.
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Grooss, O.F., Holm, C.N., Alphinas, R.A. (2021). Predicting the Product Life Cycle of Songs on the Radio. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_34
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