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.
Authors:
Ortiz, Andrew; Leider, Colby N.
Affiliation:
University of Miami, Coral Gables, FL, USA
AES Convention:
139 (October 2015)
eBrief:224
Publication Date:
October 23, 2015
Click to purchase paper as a non-member or you can login as an AES member to see more options.
No AES members have commented on this paper yet.
To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.