Publications de Gianluca Monaci

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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 ...

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 ...

This paper presents a novel method to correlate audio and visual data generated by the same physical phenomenon, based on sparse geometric representation of video sequences. The video signal is modeled as a sum of geometric primitives evolving through time ...

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 ...

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 ...

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 ...

This paper presents a methodology for extracting meaningful synchronous structures from multi-modal signals. Simultaneous processing of multi-modal data can reveal information that is unavailable when handling the sources separately. However, in natural hi ...

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 ...

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 ...

This paper presents an algorithm to correlate audio and visual data generated by the same physical phenomenon. According to psychophysical experiments, temporal synchrony strongly contributes to integrate cross-modal information in humans. Thus, we define ...

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 ...

Gianluca Monaci

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On the modelling of multi-modal data using redundant dictionaries

Learning bimodal structure in audio-visual data

Analysis of Multimodal Sequences Using Geometric Video Representations

Blind Audiovisual Source Separation Using Sparse Representations

Blind Audio-Visual Source Separation based on Sparse Redundant Representations

Geometric Video Approximation Using Weighted Matching Pursuit

Learning Multi-Modal Dictionaries

Learning Multi-Modal Dictionaries: Application to Audiovisual Data

Blind Audiovisual Separation based on Redundant Representations

Learning sparse generative models of audiovisual signals

Audiovisual Gestalts

Tracking Atoms with Particles for Audio-visual Source Localization