基于GMM模型的车辆环境下稳健扬声器位置估计

Wei-Han Liu, Chieh-Cheng Cheng, Jwusheng Hu
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引用次数: 0

摘要

本文提出了一种鲁棒的车辆环境下说话人位置估计方法。该方法将高斯混合模型(GMM)应用于麦克风阵列的相位信息处理。GMM的单个高斯分量表示两个传声器之间与位置相关的一般相位差分布。这些分布可以有效地模拟说话人的位置。考虑了传声器阵列几何形状与频带的关系,避免了混叠问题。该方法即使在近场、噪声和复杂的车辆环境中也能提供准确的估计。此外,它不仅在非视距情况下表现良好,而且在扬声器与麦克风阵列在不同距离的方向上排列的情况下也表现良好。在一辆小型货车上进行了实验,结果表明,该方法在不同信噪比情况下优于流行的多信号分类方法(MUSIC)。
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Robust speaker's location estimation in a vehicle environment using GMM models
In this work, a robust speaker's location estimation method in a vehicle environment is presented. This method applies Gaussian mixture models (GMM) to the phase information obtained from a microphone array. The individual Gaussian component of a GMM represents some general location-dependent phase difference distribution between two microphones. These distributions are effective in modeling the speaker's location. The relation between geometry of microphone array and frequency band is taken into consideration to avoid aliasing problems. The proposed approach provides an accurate estimation even in near-field, noisy and complex vehicle environment. Moreover, it performs well not only in non-line-of-sight cases, but also in the conditions that the speakers are aligned in a direction to the microphone array with difference distances. Experiments are conducted in a mini-van vehicle and the results show that the proposed method outperform the popular technique multiple signal classification method (MUSIC) in different SNR cases.
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