Grazing Detection using Deep Learning and Sentinel-2 Time Series Data
A. Pirinen, D. Fano Yela, S. Chakraborty, E. Källman , Grazing Detection using Deep Learning and Sentinel-2 Time Series Data, arXiv
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A. Pirinen, D. Fano Yela, S. Chakraborty, E. Källman , Grazing Detection using Deep Learning and Sentinel-2 Time Series Data, arXiv
C. Panigutti, D. Fano Yela, L. Porcaro, A. Bertrand, J. Soler Garrido, How to investigate algorithmic-driven risks in online platforms and search engines? A narrative review through the lens of the EU Digital Services Act, ACM Conference on Fairness, Accountability, and Transparency, June, 2025
J. Soler Garrido, S. Tolan, I. Hupont, D. Fernandez Llorca, V. Charisi, E. Gomez, H. Junklewitz, R. Hamon, D. Fano Yela, C. Panigutti, AI Watch: Artificial Intelligence Standardisation Landscape Update, Technical Report Joint Research Centre of the European Commission
J. Soler Garrido, D. Fano Yela, C. Panigutti, H. Junklewitz, R. Hamon, T. Evas, A. André, S. Scalzo, Analysis of the preliminary AI standardisation work plan in support of the AI Act, Technical Report Joint Research Centre of the European Commission
Cecilia Panigutti, Ronan Hamon, Isabelle Hupont, David Fernandez Llorca, Delia Fano Yela, Henrik Junklewitz, Salvatore Scalzo, Gabriele Mazzini, Ignacio Sanchez, Josep Soler Garrido, Emilia Gomez, The role of explainable AI in the context of the AI Act, ACM Conference on Fairness, Accountability, and Transparency, Dec 2023
Delia Fano Yela, Florian Thalmann, Vincenzo Nicosia, Dan Stowell and Mark B. Sandler, Online visibility graphs: Encoding visibility in a binary search tree, Phys. Rev. Research, April 2020
Delia Fano Yela, Dan Stowell and Mark B. Sandler, Spectral Visibility Graphs: Application to Similarity of Harmonic Signals, European Signal Processing Conference (EUSIPCO) 2019
Delia Fano Yela, Sebastian Ewert, Ken O-Hanlon and Mark B. Sandler, Shift-Invariant Kernel Additive Modelling for Audio Source Separation, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018, pp. 616–620
Delia Fano Yela, Dan Stowell and Mark B. Sandler, Does k Matter? k-NN Hubness Analysis for Kernel Additive Modelling Vocal Separation, in Proceedings of the International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA) 2018, p. 280-289.
Delia Fano Yela, Sebastian Ewert, Derry FitzGerald, and Mark B. Sandler, Interference reduction in music recordings combining kernel additive modelling and non-negative matrix factorization, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017, pp. 51–55
Delia Fano Yela, Sebastian Ewert, Derry FitzGerald, and Mark B. Sandler, On the Importance of Temporal Context in Proximity Kernels: A Vocal Separation Case Study, in Proceedings of the Audio Engineering Society (AES) International Conference on Semantic Audio, 2017.
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at the Music and Audio Technology department of De Montfort University, Leicester, UK, Virtual Lecture
at Machine Learning for Media Discovery Workshop at ICML, Virtual Conference
Conference oral presentation at EUSIPCO 2019, A Coruña, Spain
Conference poster at LVA/ICA 2018, University of Surrey, UK
Conference poster at ICASSP 2018, Calgary, Alberta
Conference presentation at ICASSP 2017, New Orleans
Conference presentation at AES International Conference on Semantic Audio 2017, Fraunhofer Institute for Integrated Circuits IIS, Erlangen
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As Senior, Queen Mary University of London,
Electronic Engineering, Undergraduate
As Senior, Queen Mary University of London,
Electronic Engineering, Undergraduate
As Senior, Queen Mary University of London,
Electronic Engineering, Undergraduate
As Teaching Assistant, Queen Mary University of London,
Electronic Engineering, Undergraduate
As Teaching Assistant, Queen Mary University of London,
Electronic Engineering, Undergraduate
As Teaching Assistant, Queen Mary University of London,
Electronic Engineering, Undergraduate
As Teaching Assistant, Queen Mary University of London,
Electronic Engineering, Undergraduate
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