I am currently a Research Scientist at Google Research, working in the Sound Understanding Group on machine learning for audio processing. My research interests include learning algorithms for audio processing using different types of supervision, including self-supervised learning and learning with noisy labels, as well as multimodal learning.
Before joining Google Research, I received my PhD at the Music Technology Group of Universitat Pompeu Fabra in Barcelona under the supervision of Dr. Xavier Serra. My PhD thesis focused on sound event classification using different types of supervision. It was partially funded by two Google Faculty Research Awards (2017 and 2018) and included two internships at Google. Some of my thesis’ highlights include the Best Audio Representation Learning Paper Award at WASPAA21, and the FSD50K paper and dataset.
I am actively involved in machine learning challenges for audio, such as DCASE, where I have served as a Challenge Task Organizer multiple times and Technical Program Co-Chair for the 2021 Workshop, as well as the HEAR 2021 NeurIPS Challenge where I was part of the Organizing Team.
I have also been involved in teaching undergraduate and master courses related to audio processing, and in Coursera’s Audio Signal Processing for Music Applications as a teaching assistant. I graduated from Technical University of Madrid (Spain) in Telecommunications Engineering and received my Master’s degree in Engineering Acoustics from Aalborg University (Denmark).
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