Two-dimensional DOA estimation of sound sources based on weighted Wiener gain exploiting two-directional microphones
Two-dimensional DOA estimation of sound sources based on weighted Wiener gain exploiting two-directional microphones
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DOI：
10.1109/tasl.2006.881699
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发表时间：
2007-02-01
影响因子：
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通讯作者：
Abe, Masato
中科院分区：
文献类型：
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作者：
Nagata, Yoshifumi;Fujioka, Toyota;Abe, Masato
提出了一种利用两个定向传声器在方位角和仰角方向上估计声源波达方向的新方法。该方法采用加权维纳增益(WWG)进行波达方向估计。WWG是我们提出的用于自动增益控制的Wiener增益的估计，以增强被加性噪声降级的语音。WWG的角度分辨率源于WWG计算中涉及的基于谱减法(SS)的降噪，这增强了来自注视方向的信号，同时抑制了来自其他方向的信号。由于WWG涉及两个通道SS，可以处理瞬时噪声，因此噪声源不需要像普通的单通道SS那样是固定的。在此基础上，我们进一步提出了一对前向旋转对称排列的定向传声器。利用麦克风提供的两个通道信号之间的时间差和幅度差来产生DOA的二维分辨率。通过计算机仿真对该方法进行了评估，并将其与三种基于互相关函数的DOA估计方法以及两种流行的高分辨率多信号分类和最小方差方法进行了比较。信源检测率和估计精度的评估结果表明，在存在多个语音源的情况下，该方法与其他方法相比具有明显的优势。
We propose a new method for estimating directions of arrival (DOAs) of sound sources, both in azimuthal and elevation angle, using two directional microphones. This method adopts weighted Wiener gain (WWG) for DOA estimation. WWG is an estimate of the Wiener gain that we proposed for use in automatic gain control to enhance speech that is degraded by additive noise. Angular resolution of WWG arises from spectral subtraction (SS)based noise reduction involved in the WWG calculation, which enhances the signal from the look direction while suppressing Signals from other directions. Because WWG involves two-channel SS, which can deal with instantaneous noise, noise sources need not to be stationary, as they must be with ordinary single-channel SS. We further propose the exploitation of a pair of directional microphones whose front directions are arranged in rotational symmetry. The time difference and amplitude difference between the two-channel signal provided by the microphones are utilized to yield a two-dimensional resolution of DOA. We evaluated the proposed method through computer simulations and compared it to three DOA estimation methods that are based on a cross-correlation function and two popular high-resolution methods of multiple signal classification and minimum variance method. Evaluation results of the source detection rate and estimation accuracy demonstrate the remarkable superiority of our method compared to the other methods in conditions where multiple speech sources exist.