Publications de Nathanaël Perraudin

Bootstrapping Language-Audio Pre-training for Music Captioning

We introduce BLAP, a model capable of generating high-quality captions for music. BLAP leverages a fine-tuned CLAP audio encoder and a pre-trained Flan-T5 large language model. To achieve effective cross-modal alignment between music and language, BLAP uti ...

Institute of Electrical and Electronics Engineers Inc.2025

Coarse-to-Fine Text-to-Music Latent Diffusion

Nathanaël Perraudin

We introduce DiscoDiff, a text-to-music generative model that utilizes two latent diffusion models to produce high-fidelity 44.1kHz music hierarchically. Our approach significantly enhances audio quality through a coarse-to-fine generation strategy, levera ...

Institute of Electrical and Electronics Engineers Inc.2025

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

Cécile Hébert, Nathanaël Perraudin, Duncan Thomas Lindsay Alexander, Guillaume Romain Obozinski

We present the development of a new algorithm which combines state-of-the-art energy-dispersive x-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spec ...

2024

espm: A Python library for the simulation of STEM-EDXS datasets

Cécile Hébert, Nathanaël Perraudin, Duncan Thomas Lindsay Alexander, Hui Chen, Guillaume Romain Obozinski

We present two open-source Python packages: "electron spectro-microscopy"(espm) and "electron microscopy tables"(emtables). The espm software enables the simulation of scanning transmission electron microscopy energy-dispersive X-ray spectroscopy datacubes ...

ELSEVIER2023

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

Nathanaël Perraudin, Andreas Loukas, Karolis Martinkus

We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors. Spectral conditioning allows for direct model ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Stationary time-vertex signal processing

Nathanaël Perraudin, Andreas Loukas

This paper considers regression tasks involving high-dimensional multivariate processes whose structure is dependent on some known graph topology. We put forth a new definition of time-vertex wide-sense stationarity, or joint stationarity for short, that g ...

2019

Forecasting Time Series With VARMA Recursions on Graphs

Nathanaël Perraudin, Andreas Loukas

2019

DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications

Nathanaël Perraudin, Michaël Defferrard

Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. So far, these neural networks (NNs) have mostly been developed for regular Euclidean domains such as those su ...

ELSEVIER SCIENCE BV2019

A Time-Vertex Signal Processing Framework

Nathanaël Perraudin, Benjamin Ricaud, Francesco Grassi, Andreas Loukas

An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate th ...

Institute of Electrical and Electronics Engineers2018

Inpainting of Long Audio Segments With Similarity Graphs

Nathanaël Perraudin

We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A sui ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Designing Gabor windows using convex optimization

Nathanaël Perraudin

Redundant Gabor frames admit an infinite number of dual frames, yet only the canonical dual Gabor system, constructed from the minimal l(2)-norm dual window, is widely used. This window function however, might lack desirable properties, e. g. good time-fre ...

ELSEVIER SCIENCE INC2018

Towards Stationary Time-Vertex Signal Processing

Pierre Vandergheynst, Nathanaël Perraudin, Francesco Grassi, Andreas Loukas

Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks. Yet, though these systems are often dynamic, state-of-the-art methods ...

Ieee2017

Large Scale Graph Learning from Smooth Signals

Nathanaël Perraudin, Vasilis Kalofolias

Graphs are a prevalent tool in data science, as they model the inherent structure of the data. They have been used successfully in unsupervised and semi-supervised learning. Typically they are constructed either by connecting nearest samples, or by learnin ...

2017

Graph-based structures in data science : fundamental limits and applications to machine learning

Nathanaël Perraudin

State-of-the-art data analysis tools have to deal with high-dimensional data. Fortunately, the inherent dimensionality of data is often much smaller, as it has an internal structure limiting its degrees of freedom. In most cases, this structure can be appr ...

EPFL2017

Fast Robust PCA on Graphs

Pierre Vandergheynst, Nathanaël Perraudin, Gilles Puy, Vasilis Kalofolias, Nauman Shahid

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

Nathanaël Perraudin, Benjamin Ricaud, Francesco Grassi

Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolv ...

2016