A multimodal approach to extract optimized audio features for speaker detection

Patricia Besson, Torsten Butz, Murat Kunt, Jean-Philippe Thiran
IEEE, 2005
Article de conférence

We present a method that exploits an information theoretic framework to extract optimal audio features with respect to the video features. A simple measure of mutual information between the resulting audio features and the video ones allows to detect the active speaker among different candidates. The results show that our method is able to exploit the shared speech information contained in audio and video signals to recover their common source.

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Patricia Besson, Torsten Butz, Murat Kunt, Jean-Philippe Thiran
IEEE, 2005
Article de conférence

Résumé

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https://infoscience.epfl.ch/record/87276?ln=fr

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Concepts associés (6)

Concepts associés

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Publications associées (6)

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Information mutuelle

Dans la théorie des probabilités et la théorie de l'information, l'information mutuelle de deux variables aléatoires est une quantité mesurant la dépendance statistique de ces variables. Elle se mesu

Théorie de l'information

La théorie de l'information, sans précision, est le nom usuel désignant la théorie de l'information de Shannon, qui est une théorie utilisant les probabilités pour quantifier le contenu moyen en inf

Vidéo

thumb|Réalisateur vidéo Le terme vidéo désigne à la fois l'ensemble des techniques permettant la constitution d'un signal électrique représentant des s animées et la restitution de celles-ci ainsi que

Extraction of Audio Features Specific to Speech using Information Theory and Differential Evolution

Patricia Besson, Murat Kunt, Vlad Popovici, Jean-Philippe Thiran, Jean-Marc Vesin

We present a method that exploits an information theoretic framework to extract optimized audio features using the video information. A simple measure of mutual information (MI) between the resulting audio features and the video ones allows to detect the active speaker among different candidates. Our method involves the optimization of an MI-based objective function. No approximation is introduced to solve this optimization problem, neither concerning the estimation of the probability density functions (pdf) of the features, nor the cost function itself. The pdf are estimated from the samples using a non-parametric approach. As far as concern the optimization process itself, three different optimization methods (one local and two globals) are compared in this paper. The Differential Evolution algorithm is shown to be outstanding performant for our problem and is threrefore eventually retains. Two information theoretic optimization criteria are compared and their ability to extract audio features specific to speeh is discussed. As a result, our method achieves a speaker detection rate of 100% on our test sequences, and of 95% on a state-of-the-art sequence.

2005

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Impact of Sample Sizes on Information Theoretic Measures for Audio-Visual Signal Processing

Ivana Arsic de Heras Ciechomska, Ninoslav Marina, Jean-Philippe Thiran

In this paper we aim to explore what is the most appropriate number of data samples needed when measuring the temporal correspondence between a chosen set of video and audio cues in a given audio-visual sequence. Presently the optimal model that connects statistics of audio and video signals does not exist since one does not know the most appropriate features to be extracted in order to analyze their correlation. Previous approaches assumed simple parametric and non-parametric models for the joint distribution for capturing the complex signal relationships. The main problem in using the standard information theoretic quantities, such as entropy and mutual information, is the accurate estimation of the probability density function from a limited number of data. The main idea is to project the data into a statistically sufficient low-dimensional subspace, suitable for density estimation. Then using a simple parametric model based on assumption of Gaussianity, mutual information is estimated and applied as a measure of correspondence. We exploit how the choice of sample size affects the reliability of the correspondence measure (mutual information) between selected features of the two modalities, audio and video.

IEEE2005

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Extraction of audio features specific to speech production for multimodal speaker detection

Patricia Besson, Murat Kunt, Vlad Popovici, Jean-Philippe Thiran, Jean-Marc Vesin

A method that exploits an information theoretic framework to extract optimized audio features using video information is presented. A simple measure of mutual information (MI) between the resulting audio and video features allows the detection of the active speaker among different candidates. This method involves the optimization of an MI-based objective function. No approximation is needed to solve this optimization problem, neither for the estimation of the probability density functions (pdf) of the features, nor for the cost function itself. The pdf are estimated from the samples using a non-parametric approach. The challenging optimization problem is solved using a global method: the Differential Evolution algorithm. Two information theoretic optimization criteria are compared and their ability to extract audio features specific to speech is discussed. Using these specific speech audio features, candidates video features are then classified as membership of the "speaker" or "non-speaker" class, resulting in a speaker detection scheme. As a result, our method achieves a speaker detection rate of 100% on home- grown test sequences, and of 85% on most commonly used sequences.

2008

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