Computer Science Seminar: Elena Georgieva (New York University)
Speaker: Elena Georgieva (New York University)
Title: Computer Science Seminar: Music Information Retrieval and Hit Songs
This seminar is co-hosted by Barnard Computer Science and the Computer Music Center at Columbia University.
This seminar will be available for in-person and Zoom participation. If you would like to receive the Zoom link, please register using the "Register" button above.
Music technology is a wonderful combination of the technical and the artistic. In this talk, I'll be discussing my computational approaches to studying, specifically, popular music. Popular music, as measured by the Billboard charts, is widely listened-to and contributes broadly to culture and society. I'll be discussing my work using machine learning to predict Billboard hits based on past data, and my current work specifically studying the vocal lines of popular songs from 1922-2010. Finally, I'll broadly talk about the field of Music Information Retrieval, current research topics in the field, and some industry career paths in music and engineering.
Elena is a PhD student at NYU's Music and Audio Research Lab where she studies music information retrieval, sound recording, and music perception/cognition. Before coming to NYU, Elena taught sound recording at Stanford University's Center for Computer Research in Music and Acoustics (CCRMA), where she received her master’s degree in 2019. So far, Elena has worked on projects related to hit song science, sound recording, vocal expression, computer music, and neuroimaging. Elena has presented her work at several international conferences, universities, and Silicon Valley tech companies. In her music life, Elena performs with several vocal groups in New York City, enjoys recording and mixing music, and is learning the drums. She also holds a Bachelor of Science degree from UCLA in Cognitive Science.