2019/08/27 by Celli, Fabio
#Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.1908.10275
Is it possible use algorithms to find trends in the history of popular music? And is it possible to predict the characteristics of future music genres? In order to answer these questions, we produced a hand-crafted dataset with the intent to put together features about style, psychology, sociology and typology, annotated by music genre and indexed by time and decade. We collected a list of popular genres by decade from Wikipedia and scored music genres based on Wikipedia descriptions. Using statistical and machine learning techniques, we find trends in the musical preferences and use time series forecasting to evaluate the prediction of future music genres.