ベイジアンネットワークに基づく視聴覚情報の統合を用いた画像からの3次元音源位置推定

概要

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This paper proposes a method to estimate sound source position by fusing auditory and visual information with Bayesian network in human-robot interaction. We firstly integrate multi-channel audio signals and a depth image about the environment to generate a likelihood map for sound source localization. However, this integration, denoted by “MICs", does not always lead to locate a sound source correctly. For correcting the failure in localization, we integrate the likelihood values generated from “MICs" and the skin-color distribution in an image according to the result of classifying audio signal into speech/non-speech categories. The audio classifier is based on support vector machine(SVM) and skin-color distribution is modeled by Gaussian mixture model (GMM). With the evidences given by MICs, SVM and GMM, we infer whether pixels in images correspond to a sound source or not according to the trained Bayesian network. Finally, experimental results are presented to show the effectiveness of the proposed method.
社団法人電気学会の論文
2004-03-01