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Two-Dimensional DOA Estimation of Sound Sources Based on Weighted Wiener Gain Exploiting Two-Directional Microphones

机译:基于加权维纳增益开发双向麦克风的二维声源DOA估计

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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
机译:我们提出了一种使用两个定向麦克风来估计声源在方位角和仰角中的到达方向(DOA)的新方法。该方法采用加权维纳增益(WWG)进行DOA估计。 WWG是我们建议用于自动增益控制的Wiener增益的估计值,该增益用于增强由于加性噪声而降低的语音。 WWG的角分辨率来自WWG计算中涉及的基于光谱减法(SS)的降噪,这可以增强来自观看方向的信号,同时抑制来自其他方向的信号。因为WWG涉及可处理瞬时噪声的两通道SS,所以噪声源不必像普通的单通道SS那样是固定的。我们进一步提出对一对定向传声器的开发,其前方向旋转对称。麦克风提供的两通道信号之间的时间差和幅度差用于产生DOA的二维分辨率。我们通过计算机仿真对提出的方法进行了评估,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的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,本方法相对于其他方法具有明显的优越性。

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