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A Probabilistic Model for Robust Localization Based on a Binaural Auditory Front-End

机译:基于双耳听觉前端的鲁棒定位概率模型

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Although extensive research has been done in the field of machine-based localization, the degrading effect of reverberation and the presence of multiple sources on localization performance has remained a major problem. Motivated by the ability of the human auditory system to robustly analyze complex acoustic scenes, the associated peripheral stage is used in this paper as a front-end to estimate the azimuth of sound sources based on binaural signals. One classical approach to localize an acoustic source in the horizontal plane is to estimate the interaural time difference (ITD) between both ears by searching for the maximum in the cross-correlation function. Apart from ITDs, the interaural level difference (ILD) can contribute to localization, especially at higher frequencies where the wavelength becomes smaller than the diameter of the head, leading to ambiguous ITD information. The interdependency of ITD and ILD on azimuth is a complex pattern that depends also on the room acoustics, and is therefore learned by azimuth-dependent Gaussian mixture models (GMMs). Multiconditional training is performed to take into account the variability of the binaural features which results from multiple sources and the effect of reverberation. The proposed localization model outperforms state-of-the-art localization techniques in simulated adverse acoustic conditions.
机译:尽管已经在基于机器的本地化领域中进行了广泛的研究,但是混响的降级效果以及多种来源对本地化性能的影响仍然是一个主要问题。受人类听觉系统强大分析复杂声学场景能力的影响,本文将关联的外围舞台用作前端,根据双耳信号估计声源的方位角。将声源定位在水平面中的一种经典方法是通过搜索互相关函数中的最大值来估计两只耳朵之间的耳间时间差(ITD)。除ITD之外,耳间水平差(ILD)可能会导致定位,特别是在波长变得小于头部直径的较高频率处,从而导致ITD信息不明确。 ITD和ILD在方位角上的相互依赖性是一个复杂的模式,它也取决于室内声学,因此可以通过方位角相关的高斯混合模型(GMM)来学习。进行多条件训练时要考虑到双耳特征的变异性,该变异性是由多种来源引起的,以及混响的影响。在模拟的不利声学条件下,提出的定位模型优于最新的定位技术。

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