NDLI: Finding geographically representative music via social media

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Finding geographically representative music via social media

Content Provider	ACM Digital Library
Author	Lei, Phoury Luo, Jiebo Joshi, Dhiraj Parker, Charles
Abstract	People can draw a myriad of semantic associations with music. The semantics can be geographical, ethnographical, society- or time-driven, or simply personal. For certain types of music, however, this semantic association is more prominent and coherent across most peoples. Such music can often serve as an ideal accompaniment for a user activity or setting (that shares the semantics of the music), especially in media authoring applications. Among the strongest associations a piece of music can have is with the geographical area from which it generates. With video-sharing in sites such as YouTube having become a norm, one would expect that music videos tagged with a geographic location keyword are representative of the respective geographical theme. However, in the past few years, the proliferance of western pop culture throughout the world has resulted in popularity of ethnic pop (resembling Western pop) that sounds quite distinct from traditional regional music. While a human expert may easily distinguish between such ethnic pop and traditional regional music, the problem of automatically differentiating between them is still new. The problem becomes more challenging with similarities in music from many different regions. In this paper, we attempt to automatically identify music with strong geographical semantics (that is, "traditional-sounding music" for different geographical regions), using only music gathered from social media sources as our training and testing data. We also explore the use of hierarchical clustering to discover relationships between the music of different cultures, again using only social media.
Starting Page	27
Ending Page	32
Page Count	6
File Format	PDF
ISBN	9781450309868
DOI	10.1145/2072529.2072538
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-11-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Crowdsourcing Audio features Representative music Metric learning
Content Type	Text
Resource Type	Article

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