FÉVOTTE , Cédric

We propose a new hybrid (or morphological) generative model that decomposes a signal into two (and possibly more) layers. Each layer is a linear combination of localised atoms from a time-frequency dictionary. One layer has a low-rank time-frequency structure while the other as a sparse structure. The time-frequency resolutions of the...

This paper introduces a robust linear model to describe hyperspectral data arising from the mixture of several pure spectral signatures. This new model not only generalizes the commonly used linear mixing model but also allows for possible nonlinear effects to be handled, relying on mild assumptions regarding these nonlinearities. Based on this...

We present a method to synthesise a visual stream driven by an audio stream. Our approach relies on the extraction of audiovisual patterns from a collection of short movie excerpts, in a training stage. The extraction is achieved by nonnegative co-factorisation of matrix representations of the audio and visual streams. The structure of the...

Many single-channel signal decomposition techniques rely on a low-rank factor-ization of a time-frequency transform. In particular, nonnegative matrix factoriza-tion (NMF) of the spectrogram – the (power) magnitude of the short-time Fourier transform (STFT) – has been considered in many audio applications. In this set-ting, NMF with the...

We introduce a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures. The new model extends the commonly used linear mixing model by introducing an additional term accounting for possible nonlinear effects, that are treated as sparsely distributed additive outliers.With the standard...

Convex nonnegative matrix factorization (CNMF) is a variant of nonnegative matrix factorization (NMF) in which the components are a convex combination of atoms of a known dictionary. In this contribution, we propose to extend CNMF to the case where the data matrix and the dictionary have missing entries. After a formulation of the problem in...

L'analyse archétypale (AA), ou factorisation convexe en matrices non-négatives (CNMF), est une variante de la factorisation en matrices non-négatives (NMF), dans laquelle les composantes obtenues sont exprimées comme une combinaison convexe d'exemples appelés archétypes. Dans cette contribution, nous proposons d'étendre AA/CNMF au...

Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates. In particular, state-of-the-art music transcription systems decompose the spectrogram of the input signal onto a dictionary of representative note spectra. The typical measures of fit used to quantify the adequacy of...

International audience

Nous présentons ici une nouvelle méthode pour une co-factorisation bi-modale en matrices non-négatives. Cetteméthode est adaptée aux situations où deux modalités sont liées par une même information sous-jacente. Elle permet uneco-factorisation dite douce, qui prend en compte la relation entre les modalités tout en évitant l'hypothèse forte d'un...

We study the Vélo'v system, a fully automated bike-sharing system in Lyon, using a temporal network representation. A decomposition of this network is proposed by using nonnegative matrix factorisation (NMF), whose the choice of parameters is discussed. This decomposition enables us to represent the temporal network as a mixture of subnetwork,...