AES Chicago Section Event – Total Variations in Vocals Over Time
This is an online event.
Recent advancements in audio processing provide a new opportunity to study musical trends using quantitative methods. In this work, we conduct an exploratory study of 43,153 vocal tracks of popular songs spanning nearly a century, from 1924 to 2010. We use source separation to extract the vocal stem and fundamental frequency (f0) estimation to analyze pitch tracks. We extract the mean pitch, total variation, and pitch class entropy of each song, conduct statistical analysis of vocal pitch across years and genres, and report significant trends in our metrics over time and between genres.
Speaker: Elena Georgieva, Phd Student
Elena is a music technology researcher, vocal producer, and singer currently living in New York City, where she is working on her PhD at NYU’s Music and Audio Research Lab. 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 research 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. As a musician, Elena performs with several vocal groups in New York City, and enjoys recording and mixing music.
Time above listed in event local time.
Recent advancements in audio processing provide a new opportunity to study musical trends using quantitative methods. In this work, we conduct an exploratory study of 43,153 vocal tracks of popular songs spanning nearly a century, from 1924 to 2010. We use source separation to extract the vocal stem and fundamental frequency (f0) estimation to analyze pitch tracks. We extract the mean pitch, total variation, and pitch class entropy of each song, conduct statistical analysis of vocal pitch across years and genres, and report significant trends in our metrics over time and between genres.
Speaker: Elena Georgieva, Phd Student
Elena is a music technology researcher, vocal producer, and singer currently living in New York City, where she is working on her PhD at NYU’s Music and Audio Research Lab. 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 research 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. As a musician, Elena performs with several vocal groups in New York City, and enjoys recording and mixing music.
Time above listed in event local time.