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On the Importance of Temporal Context in Proximity Kernels: A Vocal Separation Case Study

Published in AES International Conference on Semantic Audio (June 2017), 2017

Here we point out the importance of including a temporal context in the kernel design for music applications. Read more

Recommended citation: 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. http://www.aes.org/e-lib/browse.cfm?elib=18752

Interference reduction in music recordings combining Kernel Additive Modelling and Non-negative Matrix Factorization

Published in International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017

In this paper we introduce machine learning in the KAM framework. Read more

Recommended citation: 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 https://ieeexplore.ieee.org/abstract/document/7952116

Does k Matter? k-NN Hubness Analysis for Kernel Additive Modelling Vocal Separation

Published in International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA) 2018, 2018

We analyse the influence of the sole parameter of the KAM framework and propose an automatic graph theory method to set it. Read more

Recommended citation: 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. https://link.springer.com/chapter/10.1007/978-3-319-93764-9_27

Shift-Invariant Kernel Additive Modelling for Audio Source Separation

Published in International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

We introduce a shift-invariant kernel function in the KAM framework. Read more

Recommended citation: 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 https://ieeexplore.ieee.org/abstract/document/8461801/references#references

Spectral Visibility Graphs:Application to Similarity of Harmonic Signals

Published in European Signal Processing Conference (EUSIPCO) 2019, 2019

We introduce the visibility graph for audio spectra and propose a novel representation for audio analysis: the spectral visibility graph degree. Read more

Recommended citation: Delia Fano Yela, Dan Stowell and Mark B. Sandler, Spectral Visibility Graphs: Application to Similarity of Harmonic Signals, European Signal Processing Conference (EUSIPCO) 2019 https://arxiv.org/abs/1903.01976v3

Online visibility graphs: Encoding visibility in a binary search tree

Published in American Physical Society, 2020

Here we present a highly efficient method to compute visibility graphs, which allows, for the first time, on-line computation. Read more

Recommended citation: 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 https://link.aps.org/doi/10.1103/PhysRevResearch.2.023069

The role of explainable AI in the context of the AI Act

Published in ACM Conference on Fairness, Accountability, and Transparency, 2023

This work aims to bring some clarity as regards XAI in the context of the AI Act and focuses in particular on the AI Act requirements for transparency and human oversight. Read more

Recommended citation: 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 https://dl.acm.org/doi/pdf/10.1145/3593013.3594069

Analysis of the preliminary AI standardisation work plan in support of the AI Act

Published in Technical Report Joint Research Centre of the European Commission, 2023

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Recommended citation: 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 https://publications.jrc.ec.europa.eu/repository/handle/JRC132833

AI Watch: Artificial Intelligence Standardisation Landscape Update

Published in Technical Report Joint Research Centre of the European Commission, 2023

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Recommended citation: 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 https://publications.jrc.ec.europa.eu/repository/handle/JRC131155