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Audio-Visual Speaker Localization via Weighted Clustering
Abstract : In this paper we address the problem of detecting and locating speakers using audiovisual data. We address this problem in the framework of clustering. We propose a novel weighted clustering method based on a finite mixture model which explores the idea of non-uniform weighting of observations. Weighted-data clustering techniques have already been proposed, but not in a generative setting as presented here. We introduce a weighted-data mixture model and we formally devise the associated EM procedure. The clustering algorithm is applied to the problem of detecting and localizing a speaker over time using both visual and auditory observations gathered with a single camera and two microphones. Audiovisual fusion is enforced by introducing a cross-modal weighting scheme. We test the robustness of the method with experiments in two challenging scenarios: disambiguate between an active and a non-active speaker, and associate a speech signal with a person.
Cited literature [16 references]
https://hal.archives-ouvertes.fr/hal-01053732
Contributor : Perception Team Connect in order to contact the contributor
Submitted on : Monday, August 11, 2014 - 4:26:58 PM
Last modification on : Tuesday, October 19, 2021 - 11:13:10 PM
Long-term archiving on: : Tuesday, November 25, 2014 - 10:51:05 PM
Contributor : Perception Team Connect in order to contact the contributor
Submitted on : Monday, August 11, 2014 - 4:26:58 PM
Last modification on : Tuesday, October 19, 2021 - 11:13:10 PM
Long-term archiving on: : Tuesday, November 25, 2014 - 10:51:05 PM
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