Multiple Frequency and Source Angle Estimation by Gaussian Mixture Model with Modified Microphone Array Data Model
Multiple Frequency and Source Angle Estimation by Gaussian Mixture Model with Modified Microphone Array Data Model
复制标题
通过高斯混合模型和改进的麦克风阵列数据模型进行多频率和源角度估计
DOI：
10.2299/jsp.21.163
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
M. Fukumoto
中科院分区：
文献类型：
--
作者：
Bandhit Suksiri;M. Fukumoto
本文提出了一种有效的宽带信号一维波达方向估计方法。所提出的方法是兼容的最经典的基于子空间的方法，如传统的和根多重信号分类。虽然它只采用高斯混合模型和最大似然估计算法，但它足以表现出宽带源的角度估计。提出并研究了麦克风阵列数据模型的修正，以避免均匀线阵辐射中不必要的旁瓣引起的混淆。的性能进行评估的均方根误差在一定范围内的信号噪声比。总之，所提出的方法使信号源的合成，并提供了一个潜在的替代智能源定位系统。
This paper presents an alternative and efficient one-dimensional direction-of-arrival estimation method for wideband sources. The proposed method is compatible with most classical subspace-based methods, such as, conventional and root multiple signal classification. Although it only employs a Gaussian mixture model with a maximum likelihood estimation algorithm, it is sufficient for exhibiting wide-band sources angle estimation. Modification of the microphone array data model is proposed and investigated in order to avoid confusion caused by unwanted side lobes in uniform linear arrays radiation. The performance is evaluated in terms of the root-mean-squared error over a range of the signal-to-noise ratio. In conclusion, the proposed method enables the synthesis of signal sources and provides a potential alternative to intelligent source localization systems.