Music Information Retrieval

In music information retrieval, Dr. Ogihara is interested in integration of various types of music data (metadata, lyrics, acoustic data, listening patterns, and user social networks) for classification and recommendation.  In a recent paper with a graduate student Yajie Hu, Dr. Ogihara studied the question of modeling transitions from one genre to another in music listening behaviors using freshness in listeners’ memories that decay exponentially in time so as to make meaningful recommendations for the “next piece” to listen to.  With CCS Scientist, Dingding Wang, Dr. Ogihara studied the problem of exploring “Twitter follower” networks to improve automatic genre/style classification of artists.  A recent book with Tao Li (Florida International University) and George Tzanetakis (University of British Columbia) “Music Data Mining” surveys a variety of problems and techniques that appear in large-scale music data analysis.