Thursday, 2nd of December 2021, 12:00 – 1:00

Hit Song Prediction

Venue: 
online - link

Lecturer:
Michael Vötter, Researcher at DBIS

Abstract: 

Hit song prediction is a subfield of the music information retrieval research field. In general hit song prediction is a popularity prediction task that is modeled in different ways. The overall aim is predicting the popularity of a given song before or shortly after its release. This is of particular interest for the music industry as well as for individual artists to tailor songs towards success. From a scientific standpoint two general questions arise. (a) What influences the success of a piece of music and (b) how can we create models that are able to predict the popularity of a song? Our focus lies on the second overarching question. To answer this question in different ways of modeling the task range from binary classification (hit/popular and non-hit/unpopular) to regression that aims to predict e.g. the top position in charts or streaming metrics such as listener counts. To achieve this, different types of features and models are used. We distinguish two categories of song features: (a) internal (audio) features and (b) external features that are related to the song that cannot be derived from the song itself. Further, the community uses a wide variety of models ranging from linear models to neural networks to utilize mainly internal features to tackle the task. One major obstacle for research in this field is the availability of publicly available data. Our recently published dataset is one of the few publicly available datasets that is large in size containing numerous features for a wide range of non-exotic music and hence tries to overcome this problem.

 

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