Many of today’s popular dance music records are identifiable by a ”drop”—a section of the song that is commonly the highest in both listener-perceived and actual signal energy. In this paper we examine several computational methods for locating the exact time at which the drop occurs in a given audio sample. Various metrics are compared and contrasted based on relevant audio signal features. This technology has potential applications within automated DJ software, online music streaming services, computational ethnomusicology research, and more.
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