Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

Antoine Deleforge 1 Radu Horaud 1, * Yoav Y. Schechner 2 Laurent Girin 3
* Auteur correspondant
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
GIPSA-DPC - Département Parole et Cognition
Abstract : This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally-linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods.
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Soumis le : mercredi 4 mars 2015 - 15:38:05
Dernière modification le : jeudi 11 janvier 2018 - 06:27:21
Document(s) archivé(s) le : vendredi 5 juin 2015 - 11:05:37


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Antoine Deleforge, Radu Horaud, Yoav Y. Schechner, Laurent Girin. Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression. IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2015, 23 (4), pp.718-731. 〈〉. 〈10.1109/TASLP.2015.2405475〉. 〈hal-01112834v2〉



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