Teaching/supervision

Teaching material

I have taught different courses in the areas of Artificial Intelligence and Sound and Music Computing at Escola Superior de Música de Catalunya and Universitat Pompeu Fabra. Some recent open teaching material:

• Schedl, M., Gómez, E., Lex, E., Retrieval and Recommendation Systems at the Crossroads of Artificial Intelligence, Ethics, and Regulation, Tutorial at SIGIR 2022.
• Gómez, E., Dignum V., (Academic Coordinators) Human behaviour and machine intelligence (HUMAINT) Winter School with open educational material (slides, videos).
• Cante Flamenco Tech, MOOC, Universitat Pompeu Fabra, MiríadaX platform (2019-2020)
• Gómez, E., Holzapfel, A., Miron, M., Sturm, B. L. Fairness, Accountability and Transparency in Music Information Research, Tutorial, ISMIR 2019.
• Gómez, E., Schedl, M., Hu, X., Serra, X., Music Information Retrieval: Overview, recent developments and future challenges, Tutorial, ISMIR 2016.

PhD supervision

I am now supervising the following PhD students:

1. Marina Escobar (2020), PhD student at HUMAINT team, Universidad Politécnica de Valencia, Universitat Pompeu Fabra.
2. Carlos Entrena (2022), PhD student at HUMAINT team, Free University of Brussels, Belgium.
3. Roser Batlle (2022), PhD student at Music Technology Group, Universitat Pompeu Fabra.

PhD supervision 

1. Lorenzo Porcaro (2012), http://Assessing the Impact of Music Recommendation Diversity on ListenersAssessing the Impact of Music Recommendation Diversity on Listeners, Universitat Pompeu Fabra.
2. Juan Gómez Cañón (2022), Human-centered machine learning for music emotion recognition, Universitat Pompeu Fabra.
3. Helena Cuesta (2022), Data Driven pitch content description of choral singing recordings,  Universitat Pompeu Fabra.
4. Furkan Yesiler (2022), Data-driven musical version identification: accuracy, scalability and bias perspectives, Universitat Pompeu Fabra.
5. Pritish Chandna, (2021), Neural Networks For Singing Voice Extraction In Monaural Polyphonic Music Signals, Universitat Pompeu Fabra.
6. Blai Meléndez (2021), Relative Music Loudness Estimation In TV Broadcast Audio Using Deep Learning: an industrial perspective, co-supervised with Emilio Molina, industrial PhD with BMAT, Universitat Pompeu Fabra.
7. Andrés Pérez (2020), Parametric Analysis of Ambisonic Audio: Contributions to methods, applications and data generation, co-supervised with Adán Garriga, industrial PhD with Eurecat, Universitat Pompeu Fabra.
8. Olga Slizovskaya (2016-2020) Audio-visual approaches in music information retrieval, (co-supervised by Gloria Haro), Universitat Pompeu Fabra.
9. Perfecto Herrera (2018). MIRages: an account of music audio extractors, semantic description and context-awareness, in the three ages of MIR. My supervised supervisor (co-supervised by Xavier Serra).
10. Marius Miron (2018). Source separation methods for orchestral music: timbre-informed and score-informed strategies. (co-supervised by Jordi Janer), Universitat Pompeu Fabra.
11. Juan J. Bosch (2017), From Heuristics-Based to Data-Driven Audio Melody Extraction,  Universitat Pompeu Fabra.
12. Álvaro Sarasúa (2017), Musical Interaction Based on the Conductor Metaphor, Universitat Pompeu Fabra.
13. Agustín Martorell (2013) Modelling tonal context dynamics by temporal multi-scale analysis, Universitat Pompeu Fabra.
14. Justin Salamon (2013). Melody Extraction from Polyphonic Music Signals, Universitat Pompeu Fabra..
15. Jose Zapata (2013). Comparative Evaluation and Combination of Automatic Rhythm Description Systems, Universitat Pompeu Fabra.

I have also supervised a number of master and undergraduate students.