Hi I'm Katerina Kosta, a senior machine learning researcher at Speech, Audio and Music Intelligence (SAMI) team at ByteDance/TikTok. I pursued my PhD
from the Centre for Digital Music, Queen Mary University of
London, conducting research on modelling dynamic variations in expressive music performance. Other research interests
include custom data structures, pattern recognition and machine learning for music synthesis and analysis
of perceived emotion in music audio.
I received degrees from National and Kapodistrian University of Athens (Mathematics) and Filippos Nakas Conservatory, Athens (Piano), and a Sound and Music Computing Masters from the Music Technology Group, UPF, Barcelona.
Micchi, G., Kosta, K., Medeot, G., Chanquion, P. (2021). A deep learning method for enforcing coherence in automatic chord recognition. In Proceedings of the 22nd International Society for Music Information Retrieval Conference (ISMIR), pp. 443-451.
Medeot, G., Cherla, S., Kosta, K., McVicar, M., Abdallah, S., Selvi, M., Newton-Rex, E., & Webster, K. (2018). StructureNet: Inducing Structure in Generated Melodies. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), pp. 725-731, Paris, France.
Kosta, K., O. F. Bandtlow, E. Chew (2018). MazurkaBL: Score-aligned loudness, beat, and expressive markings data for 2000 Chopin Mazurka recordings. In Proceedings of the 4th International Conference on Technologies for Music Notation and Representation (TENOR), pp. 85-94, Montreal, Canada.
Kosta, K., O. F. Bandtlow, E. Chew (2017). Dynamic change points in music audio capture dynamic markings in score. 18th International Society for Music Information Retrieval Conference (ISMIR), Late-Breaking and Demo Session, Suzhou, China.
Kosta, K., O. F. Bandtlow, E. Chew (2016). Outliers in Performed Loudness Transitions: An Analysis of Chopin Mazurka Recordings. In Proceedings of the 14th International Conference for Music Perception and Cognition (ICMPC), pp. 601-604, July 5-9, 2016, San Francisco, California, USA.
Kosta K., R. Ramirez, O. F. Bandtlow, E. Chew (2015). Predicting loudness levels and classifying dynamic markings in recorded music. In Proceedings of 8th International Workshop on Machine Learning and Music (MML2015), Machine Learning for Music Generation, Vancouver, Canada.
Kosta, K., O. F. Bandtlow, E. Chew (2015). A Change-point Approach Towards Representing Musical Dynamics. In T. Collins, D. Meredith, A. Volk (eds.): Mathematics and Computation in Music: 5th International Conference, MCM 2015, London, UK, June 22-25, 2015, Proceedings, pp. 179-184, Lecture Notes in Computer Science 9110, Berlin: Springer.
Kosta, K., O. F. Bandtlow and E. Chew (2014). A Study of Score Context-dependent Dynamics in Piano Performance. In Proceedings of the Performance Studies Network International Conference (PSN3), Jul 17-20, Cambridge, UK.
Kosta, K., O. F. Bandtlow, E. Chew (2014). Practical Implications of Dynamic Markings in the Score: Is piano always piano? In Proceedings of the 53rd Audio Engineering Society (AES) Meeting on Semantic Audio, Jan 26-29, London, UK.
Kosta, K., Y. Song, G. Fazekas, M. Sandler (2013). A Study of Cultural Dependence of Perceived Mood in Greek Music. In Proceedings of the 14th International Society for Music Information Retrieval (ISMIR), pp. 317-322, Nov 4-8, Curitiba, Brazil.
Kosta, K., M. Marchini, H. Purwins (2012). Unsupervised Chord-Sequence Generation from an Audio Example. In Proceedings of the 13th International Society for Music Information Retrieval (ISMIR), pp. 481-486, Porto, Portugal.