Person

Gianluca Monaci

This person is no longer with EPFL

Related publications (25)

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In this paper we propose a novel method which is able to detect and separate audio-visual sources present in a scene. Our method exploits the correlation between the video signal captured with a camera and a synchronously recorded one-microphone audio trac ...

2010

Learning bimodal structure in audio-visual data

Pierre Vandergheynst, Gianluca Monaci

A novel model is presented to learn bimodally informative structures from audio-visual signals. The signal is represented as a sparse sum of audio- visual kernels. Each kernel is a bimodal function consisting of synchronous snippets of an audio waveform an ...

2009

Geometric Video Approximation Using Weighted Matching Pursuit

Michel Bierlaire, Pierre Vandergheynst, Gianluca Monaci, Oscar Divorra Escoda

In recent years, many works on geometric image representation have appeared in the literature. Geometric video representation has not received such an important attention so far, and only some initial works in the area have been presented. Works on geometr ...

2009

Blind Audiovisual Separation based on Redundant Representations

Pierre Vandergheynst, Rémi Gribonval, Gianluca Monaci, Anna Llagostera Casanovas

In this work we present a method to perform a complete audiovisual source separation without need of previous information. This method is based on the assumption that sounds are caused by moving structures. Thus, an efficient representation of audio and vi ...

2008

Learning sparse generative models of audiovisual signals

Pierre Vandergheynst, Gianluca Monaci

This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that ...

2008

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Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This par ...

2007

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We present a general framework and an efficient algorithm for tracking relevant video structures. The structures to be tracked are implicitly defined by a Matching Pursuit procedure that extracts and ranks the most important image contours. Based on the ra ...

2007

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In this work we present a method to jointly separate active audio and visual structures on a given mixture. Blind Audiovisual Source Separation is achieved exploiting the coherence between a video Signal and a one-microphone audio track. The efficient repr ...

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On the modelling of multi-modal data using redundant dictionaries

Gianluca Monaci

EPFL2007

Blind Audiovisual Source Separation Using Sparse Representations

Pierre Vandergheynst, Gianluca Monaci, Anna Llagostera Casanovas

In this work we present a method to jointly separate active audio and visual structures on a given mixture. Blind Audiovisual Source Separation is achieved exploiting the coherence between a video signal and a one-microphone audio track. The efficient repr ...

2007