|Contact:||jordi < dot > pons < at > upf < dot > edu|
|CV in english (January 2017)||GitHub|
|CV in catalan (January 2017)||Google Scholar|
- PhD candidate 2015 – till date (Barcelona)
Deep learning for music modeling.
- Pandora Internship Summer 2017 (USA, Oakland – Bay Area)
Leveraging the Music Genome Project with
supervised deep learning.
- German Hearing Center Summer 2015 (Hannover)
Improving cochlear implant users music perception using source separation.
- Master in Sound & Music Computing
2014 – 2015 (Barcelona)
UPF – Music Technology Group.
- IRCAM Internship Sept. 2013 – Aug. 2014 (Paris)
Source separation for drums transcription.
- Telecommunications Engineer 2009 – 2014 (Barcelona)
Universitat Politècnica de Catalunya.
- Conservatory 2002 – 2006 (Girona)
Piano, clarinet & harmony studies.
- AI Grant Fellowship (2017) for the creation of the Freesound Dataset.
- Machine Learning award for the poster Towards a grounded deep learning paradigm for music modeling in 5th DTIC-UPF Doctoral Student Workshop (2017).
- Best paper award for Experimenting with Musically Motivated Convolutional Neural Networks in 14th International Workshop on Content-Based Multimedia Indexing (CBMI, 2016).
Jordi Pons is a telecommunications engineer specialized in audiovisual systems (Universitat Politècnica de Catalunya, Barcelona) and he holds an M.S. in sound and music computing (Music Technology Group – Universitat Pompeu Fabra, Barcelona). Jordi did his first internship at IRCAM (Paris) where he wrote his undergraduate thesis in source separation for drums transcription under the supervision of Axel Roebel. Later on, he did an internship in the German Hearing Center (Hannover) where he developed his Master’s thesis on how to improve the perception of music in cochlear implant users using source separation – with Waldo Nogueira. Currently, he is pursuing a PhD in music technology, large-scale audio collections, and deep learning at the Music Technology Group (Universitat Pompeu Fabra, Barcelona) under the supervision of Xavier Serra. Related to his thesis, Jordi also did a summer internship at Pandora where he studied how different deep auto-tagging models perform at scale.