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Automatic Speaker Localization and Tracking: Using a Fusion of the Filtered Correlation with the Energy Differential

Automatic Speaker Localization and Tracking: Using a Fusion of the Filtered Correlation with the Energy Differential

Siham Ouamour, Halim Sayoud, Salah Khennouf
Copyright: © 2010 |Volume: 2 |Issue: 3 |Pages: 19
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781609609436|DOI: 10.4018/jmcmc.2010070102
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MLA

Ouamour, Siham, et al. "Automatic Speaker Localization and Tracking: Using a Fusion of the Filtered Correlation with the Energy Differential." IJMCMC vol.2, no.3 2010: pp.15-33. http://doi.org/10.4018/jmcmc.2010070102

APA

Ouamour, S., Sayoud, H., & Khennouf, S. (2010). Automatic Speaker Localization and Tracking: Using a Fusion of the Filtered Correlation with the Energy Differential. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 2(3), 15-33. http://doi.org/10.4018/jmcmc.2010070102

Chicago

Ouamour, Siham, Halim Sayoud, and Salah Khennouf. "Automatic Speaker Localization and Tracking: Using a Fusion of the Filtered Correlation with the Energy Differential," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 2, no.3: 15-33. http://doi.org/10.4018/jmcmc.2010070102

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Abstract

This paper presents a system of speaker localization for a purpose of speaker tracking by camera. The authors use the information given by the two microphones, placed in opposition, to determine the position of the active speaker in trying to supervise the audio-visual recording. To achieve the speaker localization task, the authors have proposed and employed two methods, which are called respectively: the filtered correlation method and the energy differential method. The principle of the first method is based on the calculation of the correlation between the two signals collected by the two microphones and a special filtering. The second is based on the computation of the logarithmic energy differential between these two signals. However, when different methods are used simultaneously to make a decision, it is often interesting to use a fusion technique combining those estimations or decisions in order to enhance the system performances. For that purpose, this paper proposes two fusion techniques operating at the decision level which are used to fuse the two estimations into one that should be more precise.

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