Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Content-based retrieval for music collections
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures
Computer Music Journal
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Automatic Generation of Music Slide Show Using Personal Photos
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Matching Songs to Events in Image Collections
ICSC '09 Proceedings of the 2009 IEEE International Conference on Semantic Computing
Suggesting Songs for Media Creation Using Semantics
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Geotagging in multimedia and computer vision--a survey
Multimedia Tools and Applications
Combining demographic data with collaborative filtering for automatic music recommendation
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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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.