CV

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Currently, I work for the Earth Species Project as a Senior AI Research Scientist. My research area is machine learning for biology applications, in particular bioacoustics. I train generative large language models and develop signal processing methods to help biologists decode animal communication.

Previously, I worked as a Senior Researcher for Music Technology Group, Pompeu Fabra University. I lead research projects, supervised PhD, Masters students and other researchers. I contributed to the overall activities and goals of the MTG. In particular, I collaborated at writing project proposals, I advised a few industrial-transfer project related to audio source separation, audio fingerprinting, music classification, version identification. In addition, I taught the Music Information Retrieval class at the Sound and Music Computing Masters.

I was a Postdoctoral researcher for European Commission’s Joint Research Centre within the project HUMAINT on Explainability and Fairness in Machine Learning. I have a PhD (2018) in Computer Science (Audio Signal Processing and Machine Learning) from Pompeu Fabra University, Barcelona.

In 2018 I defended my PhD at Music Technology Group, Pompeu Fabra University, Barcelona as a part of an European FP7 project which was aiming at transforming the way classical music is enjoyed. My PhD thesis concerned separating the audio corresponding to the instruments in an orchestral music mixture. I gave a new flavour to the topic by proposing a deep learning approach.

I have completed internships at Computational Perception Group, Johannes Kepler University, Linz where I started my work on deep learning for source separation, and at Telefonica Research, Barcelona, where I gave a more general flavour to my research career working on catastrophic forgetting in machine learning (ICML 2018 paper)

My journey in research started in 2010. Having more than 6 years experience as a software engineer, in 2010 I gave a new start to my career by moving to Barcelona to start a Master’s in signal processing and machine learning applied to music. Then I worked as a Research engineer for the research institute INESC in Porto, where I was involved in a project aiming at modelling groove, and I developed a few real-time tools for audio analysis and transformation.

I code in Python and C++, mainly using open source library and I try to publish everything I do as open source. I aim at making my work transparent and reproducible, so I can foster knowledge, development, and creativity.