Music Informatics
The area of music informatics aims to understand ‘music’, as a cultural phenomenon, through computational techniques. This may include computational musicology, automated composition, machine listening, and more. Data considered in this area is not limited to music audio alone: other relevant data includes song meta-data (including its lyrics and possibly score), as well as data about bands, artists, and their fans found e.g. on music publishing websites, blogs, social media, and more.
Our expertise centers mostly around machine listening (particularly mood estimation and chord estimation based on music audio), as well as the analysis of web, social media, and audio data in an effort to understand what is happening in the global popular music scene.
Staff
Tijl De Bie, Paolo Simeone, Jefrey Lijffijt
Researchers
Bo Kang, Ahmad Mel, Florian Adriaens
Projects
- Odysseus Grant “Exploring Data: Theoretical Foundations and Applications to Web, Multimedia, and Omics Data”.
- EPSRC Grant “Data Science for the Detection of Emerging Music Styles” DS4DEMS.
Key publications
- McVicar, Matthew, Cedric Mesnage, Jefrey Lijffijt, and Tijl De Bie. 2015. “Interactively Exploring Supply and Demand in the UK Independent Music Scene.” In Lecture Notes in Artificial Intelligence, 9286:289–292. Berlin, Germany: SPRINGER-VERLAG BERLIN.
- Ni, Yizhao, Matt McVicar, Raul Santos-Rodriguez, and Tijl De Bie. 2012. “An End-to-end Machine Learning System for Harmonic Analysis of Music.” Ieee Transactions on Audio Speech and Language Processing 20 (6): 1771–1783.
- McVicar, Matt, Raul Santos-Rodriguez, Yizhao Ni, and Tijl De Bie. 2014. “Automatic Chord Estimation from Audio: a Review of the State of the Art.” Ieee-acm Transactions on Audio Speech and Language Processing 22 (2): 556–575.
- Ni, Yizhao, Matt McVicar, Raul Santos-Rodriguez, and Tijl De Bie. 2013. “Understanding Effects of Subjectivity in Measuring Chord Estimation Accuracy.” Ieee Transactions on Audio Speech and Language Processing 21 (12): 2607–2615.
- Ni, Yizhao, Matt McVicar, Raul Santos-Rodriguez, and Tijl De Bie. 2012. “An End-to-end Machine Learning System for Harmonic Analysis of Music.” Ieee Transactions on Audio Speech and Language Processing 20 (6): 1771–1783.
