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Nicolas Farrugia - IEEE Xplore Author Profile
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Nicolas Farrugia

Publications
20
Citations
120
Publications by Year
20072024
Co-Authors:
Show All Co-Authors (28)
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Nicolas Farrugia

Affiliation

IMT Atlantique, CNRS, Lab-STICC

Publication Topics

Biography

Nicolas Farrugia received the electrical engineer degree in signal processing and the M.Sc. degree in signal and image processing from ENSEA, Cergy-Pontoise, France, both in 2005, and the Ph.D. degree in parallel embedded face detection from the Université de Bourgogne and Orange Labs, Grenoble, France, in 2008, working on the hardware implementation of deep neural networks.,In 2010, he moved to the field of cognitive neurosciences as a Postdoctoral Researcher, with a focus on the auditory cognitive neurosciences using methods such as electroencephalography and functional MRI, as well as behavioral psychology methods and motion capture. He joined IMT Atlantique in 2016 as an Associate Professor to engage in a transdisciplinary effort, combi...
Publications
20
Citations
120
Publications by Year
20072024
Co-Authors:
Show All Co-Authors (28)
Author's Published Works

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Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds

Ilyass Moummad;Nicolas Farrugia;Romain Serizel;Jeremy Froidevaux;Vincent Lostanlen

2024 32nd European Signal Processing Conference (EUSIPCO)
Year: 2024 | Conference Paper |
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others. This paper focuses on the specific case of classifying anuran species sounds using the dataset AnuraSet, that contains both class imbalance and multi-label examples. To address these ...Show More

Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds

Ilyass Moummad;Nicolas Farrugia;Romain Serizel;Jeremy Froidevaux;Vincent Lostanlen

Year: 2024 | Conference Paper |

Unsupervised Adaptive Deep Learning Method for BCI Motor Imagery Decoding

Yassine El Ouahidi;Giulia Lioi;Nicolas Farrugia;Bastien Pasdeloup;Vincent Gripon

2024 32nd European Signal Processing Conference (EUSIPCO)
Year: 2024 | Conference Paper |
In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision. Interestingly, our method does not require retraining the model, as it consists in using a frozen efficient deep learning backbone while continuously realigning data, both at input and latent spaces, based on streaming observatio...Show More

Unsupervised Adaptive Deep Learning Method for BCI Motor Imagery Decoding

Yassine El Ouahidi;Giulia Lioi;Nicolas Farrugia;Bastien Pasdeloup;Vincent Gripon

Year: 2024 | Conference Paper |

A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding

Yassine El Ouahidi;Vincent Gripon;Bastien Pasdeloup;Ghaith Bouallegue;Nicolas Farrugia;Giulia Lioi

IEEE Transactions on Neural Systems and Rehabilitation Engineering
Year: 2024 | Volume: 32 | Journal Article |
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline that achieves high classification accuracy, using only standard ingredients from the literature, to serve as a standard for comparison. The proposed architecture is composed of standard layers, including 1D convolutions, ...Show More

A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding

Yassine El Ouahidi;Vincent Gripon;Bastien Pasdeloup;Ghaith Bouallegue;Nicolas Farrugia;Giulia Lioi

Year: 2024 | Volume: 32 | Journal Article |

Self-Supervised Learning for Few-Shot Bird Sound Classification

Ilyass Moummad;Nicolas Farrugia;Romain Serizel

2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost. This is pertinent in bioacoustics, where biologists routinely collect extensive sound datasets from the natural environment. In this study, we demonstrate that SSL is capable of acquiring meaningful representations of...Show More

Self-Supervised Learning for Few-Shot Bird Sound Classification

Ilyass Moummad;Nicolas Farrugia;Romain Serizel

Year: 2024 | Conference Paper |

Regularized Contrastive Pre-Training for Few-Shot Bioacoustic Sound Detection

Ilyass Moummad;Nicolas Farrugia;Romain Serizel

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio. Deep learning systems can help achieve this goal. However, it is difficult to acquire sufficient annotated data to train these systems from scratch. To address this limitation, the Detection and Classification of Acoustic Scenes and Events (DCASE) community has r...Show More

Regularized Contrastive Pre-Training for Few-Shot Bioacoustic Sound Detection

Ilyass Moummad;Nicolas Farrugia;Romain Serizel

Year: 2024 | Conference Paper |

Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning

Ilyass Moummad;Nicolas Farrugia

2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
Year: 2023 | Conference Paper |
Cited by: Papers (13)
Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in the low data regime. In this study, we address this issue using supervised contrastive learning combined with available metadata to solve multiple pretext tasks that learn a good re...Show More

Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning

Ilyass Moummad;Nicolas Farrugia

Year: 2023 | Conference Paper |

Trainable dynamical estimation of above-surface wind speed using underwater passive acoustics

Matteo Zambra;Dorian Cazau;Nicolas Farrugia;Alexandre Gensse;Sara Pensieri;Roberto Bozzano;Ronan Fablet

OCEANS 2023 - Limerick
Year: 2023 | Conference Paper |
Covering more than 70% of Earth surface, oceans play a key role in climate regulation, are the main medium of world commercial trade and are a source of renewable energy, to cite few aspects. Despite its importance, ocean surface state reconstruction poses some challenges, due to its non-linear behavior and the heterogeneity of the spatio-temporal scales involved. State-of-the-art techniques for f...Show More

Trainable dynamical estimation of above-surface wind speed using underwater passive acoustics

Matteo Zambra;Dorian Cazau;Nicolas Farrugia;Alexandre Gensse;Sara Pensieri;Roberto Bozzano;Ronan Fablet

Year: 2023 | Conference Paper |

Learning-Based Temporal Estimation of In-Situ Wind Speed From Underwater Passive Acoustics

Matteo Zambra;Dorian Cazau;Nicolas Farrugia;Alexandre Gensse;Sara Pensieri;Roberto Bozzano;Ronan Fablet

IEEE Journal of Oceanic Engineering
Year: 2023 | Volume: 48, Issue: 4 | Journal Article |
Wind speed retrieval at the sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologies, especially satellite sensors, provide complementary means to monitor wind speed. As sea-surface winds produce sounds that propagate underwater, underwater acoustics recordings can also deliver fine-grained wind-r...Show More

Learning-Based Temporal Estimation of In-Situ Wind Speed From Underwater Passive Acoustics

Matteo Zambra;Dorian Cazau;Nicolas Farrugia;Alexandre Gensse;Sara Pensieri;Roberto Bozzano;Ronan Fablet

Year: 2023 | Volume: 48, Issue: 4 | Journal Article |

Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities

Yassine El Ouahidi;Lucas Drumetz;Giulia Lioi;Nicolas Farrugia;Bastien Pasdeloup;Vincent Gripon

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase the amount of data available and hence obtain good generalization results. To this end, we introduce a spatial graph signal interpolation technique, that allows to interpolate efficiently multiple electrodes. We conduct...Show More

Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities

Yassine El Ouahidi;Lucas Drumetz;Giulia Lioi;Nicolas Farrugia;Bastien Pasdeloup;Vincent Gripon

Year: 2023 | Conference Paper |

Pruning Graph Convolutional Networks to Select Meaningful Graph Frequencies for FMRI Decoding

Yassine El Ouahidi;Hugo Tessier;Giulia Lioi;Nicolas Farrugia;Bastien Pasdeloup;Vincent Gripon

2022 30th European Signal Processing Conference (EUSIPCO)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the brain. In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals. To this end, we introduce a deep learning architecture and adapt a pruning met...Show More

Pruning Graph Convolutional Networks to Select Meaningful Graph Frequencies for FMRI Decoding

Yassine El Ouahidi;Hugo Tessier;Giulia Lioi;Nicolas Farrugia;Bastien Pasdeloup;Vincent Gripon

Year: 2022 | Conference Paper |

Similarity between Base and Novel Classes: a Predictor of the Performance in Few-Shot Classification of Brain Activation Maps?

Myriam Bontonou;Nicolas Farrugia;Vincent Gripon

2021 55th Asilomar Conference on Signals, Systems, and Computers
Year: 2021 | Conference Paper |
The classification of neuroimaging data, also known as brain decoding, is often limited by the lack of training data. Recently, few-shot learning methods have been developed to take advantage of deep neural networks in a context where few examples are available. Some of these methods have shown to be promising in few-shot classification of brain activation maps. They involve a pretraining phase on...Show More

Similarity between Base and Novel Classes: a Predictor of the Performance in Few-Shot Classification of Brain Activation Maps?

Myriam Bontonou;Nicolas Farrugia;Vincent Gripon

Year: 2021 | Conference Paper |

Few-Shot Decoding of Brain Activation Maps

Myriam Bontonou;Giulia Lioi;Nicolas Farrugia;Vincent Gripon

2021 29th European Signal Processing Conference (EUSIPCO)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
Few-shot learning addresses problems for which a limited number of training examples are available. So far, the field has been mostly driven by applications in computer vision. Here, we are interested in adapting recently introduced few-shot methods to solve problems dealing with neuroimaging data, a promising application field. To this end, we create a neuroimaging benchmark dataset for few-shot ...Show More

Few-Shot Decoding of Brain Activation Maps

Myriam Bontonou;Giulia Lioi;Nicolas Farrugia;Vincent Gripon

Year: 2021 | Conference Paper |

Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Ghouthi Boukli Hacene;Vincent Gripon;Matthieu Arzel;Nicolas Farrugia;Yoshua Bengio

2020 18th IEEE International New Circuits and Systems Conference (NEWCAS)
Year: 2020 | Conference Paper |
Cited by: Papers (4)
Deep Neural Networks (DNNs) in general and Convolutional Neural Networks (CNNs) in particular are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large amount of parameters they contain leads to a high computational complexity and strongly limits their usability in budget-constrained devices such as embedded devices. In this paper, we pr...Show More

Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Ghouthi Boukli Hacene;Vincent Gripon;Matthieu Arzel;Nicolas Farrugia;Yoshua Bengio

Year: 2020 | Conference Paper |

Efficient Hardware Implementation of Incremental Learning and Inference on Chip

Ghouthi B. Hacene;Vincent Gripon;Nicolas Farrugia;Matthieu Arzel;Michel Jezequel

2019 17th IEEE International New Circuits and Systems Conference (NEWCAS)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end we propose an efficient hardware implementation of a recently introduced incremental learning procedure that achieves state-of-the-art performance by combining transfer learning with majority votes and quantization techniques. The proposed design is able to accommodate ...Show More

Efficient Hardware Implementation of Incremental Learning and Inference on Chip

Ghouthi B. Hacene;Vincent Gripon;Nicolas Farrugia;Matthieu Arzel;Michel Jezequel

Year: 2019 | Conference Paper |

Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging

Yusuf Yigit Pilavci;Nicolas Farrugia

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
Graph Signal Processing has become a very useful framework for signal operations and representations defined on irregular domains. Exploiting transformations that are defined on graph models can be highly beneficial when the graph encodes relationships between signals. In this work, we present the benefits of using Spectral Graph Wavelet Transform (SGWT) as a feature extractor for machine learning...Show More

Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging

Yusuf Yigit Pilavci;Nicolas Farrugia

Year: 2019 | Conference Paper |

Evaluating graph signal processing for neuroimaging through classification and dimensionality reduction

Mathilde Ménoret;Nicolas Farrugia;Bastien Pasdeloup;Vincent Gripon

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Year: 2017 | Conference Paper |
Cited by: Papers (40)
Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs...Show More

Evaluating graph signal processing for neuroimaging through classification and dimensionality reduction

Mathilde Ménoret;Nicolas Farrugia;Bastien Pasdeloup;Vincent Gripon

Year: 2017 | Conference Paper |

Incremental learning on chip

Ghouthi Boukli Hacene;Vincent Gripon;Nicolas Farrugia;Matthieu Arzel;Michel Jezequel

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Year: 2017 | Conference Paper |
Cited by: Papers (4)
Learning on chip (LOC) is a challenging problem in which an embedded system learns a model and uses it to process and classify unknown data, while adapting to new observations or classes. It may require intensive computations and complex hardware implementations to adapt to new data. We address this issue by introducing an incremental learning method based on the combination of a pre-trained Convo...Show More

Incremental learning on chip

Ghouthi Boukli Hacene;Vincent Gripon;Nicolas Farrugia;Matthieu Arzel;Michel Jezequel

Year: 2017 | Conference Paper |

Budget restricted incremental learning with pre-trained convolutional neural networks and binary associative memories

Ghouthi Boukli Hacene;Vincent Gripon;Nicolas Farrugia;Matthieu Arzel;Michel Jezequel

2017 IEEE International Workshop on Signal Processing Systems (SiPS)
Year: 2017 | Conference Paper |
Cited by: Papers (3)
Thanks to their ability to absorb large amounts of data, Convolutional Neural Networks (CNNs) have become state-of-the-art in numerous vision challenges, sometimes even on par with biological vision. They rely on optimisation routines that typically require intensive computational power, thus the question of embedded architectures is a very active field of research. Of particular interest is the p...Show More

Budget restricted incremental learning with pre-trained convolutional neural networks and binary associative memories

Ghouthi Boukli Hacene;Vincent Gripon;Nicolas Farrugia;Matthieu Arzel;Michel Jezequel

Year: 2017 | Conference Paper |

Fast and Robust Face Detection on a Parallel Optimized Architecture Implemented on FPGA

Nicolas Farrugia;Franck Mamalet;SÉbastien Roux;Fan Yang;Michel Paindavoine

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2009 | Volume: 19, Issue: 4 | Journal Article |
Cited by: Papers (28)
In this paper, we present a parallel architecture for fast and robust face detection implemented on FPGA hardware. We propose the first implementation that meets both real-time requirements in an embedded context and face detection robustness within complex backgrounds. The chosen face detection method is the Convolutional Face Finder (CFF) algorithm, which consists of a pipeline of convolution an...Show More

Fast and Robust Face Detection on a Parallel Optimized Architecture Implemented on FPGA

Nicolas Farrugia;Franck Mamalet;SÉbastien Roux;Fan Yang;Michel Paindavoine

Year: 2009 | Volume: 19, Issue: 4 | Journal Article |

A Parallel Face Detection System Implemented on FPGA

Nicolas Farrugia;Franck Mamalet;Sebastien Roux;Fan Yang;Michel Paindavoine

2007 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2007 | Conference Paper |
Cited by: Papers (4)
In this paper, we introduce a methodology for designing a system for face detection and its implementation on FPGA. The chosen face detection method is the well-known Convolutional Face Finder (CFF) algorithm, which consists in a pipeline of convolutions and subsampling operations. Our goal is to define a parallel architecture able to process efficiently this algorithm. We present a dataflow based...Show More

A Parallel Face Detection System Implemented on FPGA

Nicolas Farrugia;Franck Mamalet;Sebastien Roux;Fan Yang;Michel Paindavoine

Year: 2007 | Conference Paper |

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