About

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

She is currently a Research Scientist at Zoundream working on data-driven, audio-based infant voice analysis, supporting Zoundream's mission of providing continuous and non-invasive monitoring of infants' health. More specifically, our research focuses on the analysis of cry signals to detect signs of potential neurodevelopmental disorders and other pathologies.

Helena 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. Before Zoundream, she was a Research Scientist at DAACI and TMC2 working on deep learning for music and audio processing.

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).