My main research interest is currently the automatic analysis of pitch content in polyphonic singing audio recordings, i.e., vocal ensembles and choirs. 

In our work we try to answer the following research questions:
    • Can we extract the frequencies sung by all singers of an ensemble?
    • How can we model unison performances, where the melodic and linguistic content is shared among several singers?
    • Do singers of an ensemble interact with each other while performing?
    • Is it possible to assess the performance of a singer of an ensemble using intonation descriptors?

To answer these questions, we use signal processing and machine learning techniques for (multiple) F0 estimation, unison singing analysis and synthesis, singing performance assessment, and measuring interdependence between singers’ performances. 

As part of the PhD thesis, we worked on two publicly-available datasets of polyphonic singing voice: the Choral Singing Dataset and the Dagstuhl ChoirSet. See the Datasets & Software section for more details!

This PhD thesis is partially developed in the scope of the TROMPA (Towards Richer Online Music Public-domain Archives) project, funded by the EU’s H2020 program. https://trompamusic.eu/