The Wiki Music Dataset: a Tool for Computational Analysis of Popular Music
The Wiki Music dataset: A tool for computational analysis of popular music Fabio Celli Profilio Company s.r.l. via sommarive 18, 38123 Trento, Italy Email: fabio@profilio.co Abstract—Is it possible use algorithms to find trends in monic and timbral properties that brought changes in music the history of popular music? And is it possible to predict sound around 1964, 1983 and 1991 [14]. Beside these research the characteristics of future music genres? In order to answer fields, there is a trend in the psychology of music that studies these questions, we produced a hand-crafted dataset with the how the musical preferences are reflected in the dimensions intent to put together features about style, psychology, sociology of personality [11]. From this kind of research emerged the and typology, annotated by music genre and indexed by time MUSIC model [20], which found that genre preferences can and decade. We collected a list of popular genres by decade from Wikipedia and scored music genres based on Wikipedia be decomposed into five factors: Mellow (relaxed, slow, and ro- descriptions. Using statistical and machine learning techniques, mantic), Unpretentious, (easy, soft, well-known), Sophisticated we find trends in the musical preferences and use time series (complex, intelligent or avant-garde), Intense (loud, aggressive, forecasting to evaluate the prediction of future music genres. and tense) and Contemporary (catchy, rhythmic or danceable). Is it possible to find trends in the characteristics of the genres? Keywords—Popular Music, Computational Music analysis, And is it possible to predict the characteristics of future genres? Wikipedia, Natural Language Processing, dataset To answer these questions, we produced a hand-crafted dataset with the intent to put together MUSIC, style and sonic features, I.
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