About the Authors#
Magdalena Fuentes is an Assistant Professor at the Music and Audio Research Lab (MARL) and the Integrated Design and Media (IDM) Program at New York University (NYU), affiliated with both the Steinhardt School of Culture, Education, and Human Development and Tandon School of Engineering. Previously, she was a Provost Postdoctoral Fellow at NYU’s Center for Urban Science and Progress (CUSP) and MARL. She completed her Ph.D. in Image and Signal Processing at Université Paris-Saclay, and a B.Eng. in Electrical Engineering from Universidad de la República, Uruguay. Her research focuses on machine listening—a field at the intersection of signal processing and machine learning—where she develops models for understanding natural and everyday sounds, music, and multimodal data. Magdalena is actively involved with the IEEE Audio and Acoustic Signal Processing Technical Committee and regularly serves as an Area Chair/Meta-reviewer for ICASSP and ISMIR. She has held Program Chair roles for DCASE 2021, 2023, and 2025, as well as ISMIR 2025. Her research has been sponsored by NYU, Google and the NIH.
Giovana Morais is a PhD student in the Computer Science program working with Dr. Magdalena Fuentes at the New York University (NYU) Tandon School of Engineering. She also holds a Bachelor’s degree in Computer Science from Universidade Federal de São Carlos (UFSCar Sorocaba) and a Master’s Degree in Computer Science from Universidade de São Paulo (USP), where she developed self-supervised tempo estimation methods. Giovana’s current research interests include beat-tracking and multimodal representation learning.
Richa Namballa is a PhD student in the Music Technology program at New York University (NYU) under the supervision of Dr. Magdalena Fuentes. Her primary research areas are source separation and the representation of diverse music traditions in music information research. Richa also received her M.M. in Music Technology from NYU where she completed her thesis under the advisement of Dr. Brian McFee. Prior to pursuing her graduate studies, she worked as a senior data scientist in industry, resulting in two patents on proprietary technology she developed. Richa also holds a B.A. in Statistics and a B.S. in Business Administration from the University of California, Berkeley.
Xavier Juanola is a PhD student in the Intelligent Multimodal Vision Analysis (IMVA) group of the Universitat Pompeu Fabra (UPF), Spain since 2022. He holds B.Sc in Theoretical Physics (Astrophysics and Cosmology) from the University of Barcelona (UB), M.Sc. in High Energy Physics, Astrophysics and Cosmology from the Autonomous University of Barcelona (UAB) and M.Sc. in Intelligent Interactive Systems from Universitat Pompeu Fabra (UPF). His research focuses on applying deep learning and signal processing to both video and audio signals, with applications in machine listening and visual sound source localization.
Lucas Simões Maia is a Patent Examiner at the Instituto Nacional da Propriedade Industrial (INPI). He holds a Doctor of Science (DSc) degree in Electrical Engineering from COPPE/UFRJ. His research interests include music information retrieval, beat and downbeat tracking, and rhythm pattern recognition. Recently, Lucas published an article in the TISMIR journal about training state-of-the-art beat trackers using limited data. Additionally, he had a paper accepted for ISMIR 2024 that focuses on the analysis of Candomblé bell patterns.
Martin Rocamora is a Tenure-track Professor at the Music Technology Group (MTG) of the Universitat Pompeu Fabra (UPF), Spain. Before that, he was Senior Researcher at the MTG from July 2023 to September 2024. He has been Assistant Professor in Signal Processing at Universidad de la República (UDELAR), Uruguay, since 2013. He holds B.Sc, M.Sc., and D.Sc. degrees in Electrical Engineering from the School of Engineering, UDELAR. He was Teaching Assistant in Music Technology at the School of Music, UDELAR. His research focuses on applying machine learning and signal processing to audio signals, with applications in machine listening, music information retrieval, and computational musicology.