About

Helena Cuesta is an engineer, reseacher, and musician based in Barcelona, with special interest in audio & music signal processing and analysis, music information retrieval (MIR), and machine learning.

She is currently a Research Scientist at DAACI and TMC2 working on deep learning for music and audio processing. She holds a PhD in Music Information Retrieval from the Music Technology Group (UPF, Barcelona, 2022), supervised by Dr. Emilia Gómez. Her PhD dissertation explored data-driven techniques for multiple F0 streaming of polyphonic vocal music, being the machine learning-based computational analysis of vocal music her main field of study. During that time, she also collaborated in a multidisciplinary EU-funded project and worked on the creation and release of multiple open-access datasets of vocal music.

Helena holds a Bachelor's degree in Audiovisual Systems Engineering from Universitat Pompeu Fabra (2011-2015), a Specialist Diploma on Data Mining and Analysis from Universitat Oberta de Catalunya (2016), and a Master's degree in Sound and Music Computing from Universitat Pompeu Fabra (2016-2017).