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首页> 外文会议>IEEE/RSJ International Conference on Intelligent Robots and Systems;IROS 2012 >Unified auditory functions based on Bayesian topic model
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Unified auditory functions based on Bayesian topic model

机译：基于贝叶斯主题模型的统一听觉功能

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摘要

Existing auditory functions for robots such as sound source localization and separation have been implemented in a cascaded framework whose overall performance may be degraded by any failure in its subsystems. These approaches often require a careful and environment-dependent tuning for each subsystems to achieve better performance. This paper presents a unified framework for sound source localization and separation where the whole system is integrated as a Bayesian topic model. This method improves both localization and separation with a common configuration under various environments by iterative inference using Gibbs sampling. Experimental results from three environments of different reverberation times confirm that our method outperforms state-of-the-art sound source separation methods, especially in the reverberant environments, and shows localization performance comparable to that of the existing robot audition system.
机译：机器人的现有听觉功能（例如声源定位和分离）已在级联框架中实现，其子系统的任何故障都可能降低整体性能。这些方法通常需要对每个子系统进行仔细的环境相关调整，以实现更好的性能。本文提出了一个统一的声源定位和分离框架，其中整个系统作为贝叶斯主题模型进行了集成。该方法通过使用Gibbs采样进行迭代推理，可以在各种环境下使用通用配置改进定位和分离。来自三种不同混响时间的环境的实验结果证实，我们的方法优于最新的声源分离方法，尤其是在混响环境中，并显示了与现有机器人试听系统相当的定位性能。

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