Identification of sound source position or direction based on the statistical independecy in an actual sound environment

声源定位问题在声学的各个实际领域中都很重要，已经发展了许多方法，如MUSIC等。大多数方法只利用观测的低阶统计量，如协方差矩阵和目标信号与噪声之间的正交性(或不相关性)。近年来，独立分量分析(ICA)成为解决信号盲分离问题的热门方法，该问题是将独立的信号源混合后由多个传感器进行测量。对于源信号之间存在时差或受系统(房间等)影响的情况，也提出了复杂信号的独立分量分析。具体地，它们中的大多数是基于源信号之间或源信号与附加噪声之间的统计独立性。我们在2001年和2002年取得的结果如下。[…More 2001年]：作为研究的第一步，引入了相关方法来计算每个麦克风观测之间的延迟时间，然后估计声方向。第二种方法是利用MUSIC方法估计声源的方向，卡尔曼滤波方法检测声源的幅度信息进行声源定位；第三种方法是基于复ICA估计时间延迟或接收器与声源之间的距离，特别是对于含有时间延迟的混合观测。此外，我们可以很容易地构造一个逆滤波器来利用ICA估计的参数来分离(全部)实混合信号，而通常的复数ICA需要综合所有的频率分量。最后，通过基本仿真，验证了高质量的盲分离以及对声源参数估计的有效性。[2002年]：继2001年对声源定位问题的基本考虑之后，广泛的仿真表明，所提出的方法也使我们能够找到到达方向。这些结果已在国内或国际会议上公布。较少

Sound source localization problem has been important in various actual fields of acoustics and many approaches have been developed e.g., MUSIC, etc. Most of them utilize only the lower order statistics of observations such as a covariance matrix and the orthogonality (or uncorrelation) between the objective signal and noise. Recently , Independent Component Analysis (ICA) becomes popular to solve a blind signal separation problem where independent sources are mixed and then measured by multiple sensors. The ICA of complex signals has been also proposed for a case when observations have time differences between source signals or are affected by system (room etc.).In this research, some trials of estimating the sound source positions or directions are proposed from various viewpoints. In particular, most of them are based on the statistical independency between source signals or between the source signal and an additional noise. The results we obtained in 2001 and 2002 are listed below.[ … More 2001 year]: As the first step of research, the correlation method is introduced to calculate the delay time between observations at each microphone and then to estimate the sound direction. In the second approach, MUSIC method and Kalman filter are employed, respectively, to estimate the direction of source and to detect the amplitude information for sound source localization.The third method is a trial of estimating the time delays or the distances between receivers and sound sources based on the complex ICA, especially for the mixed observations with time delays. Further, we can easily construct an inverse filter to separate the (over-all) real mixed signals using the parameters estimated by ICA, while the usual complex ICA required synthesizing all frequency components. Finally, through fundamental simulation, we demonstrate the high-quality blind separation as well as the validity for the estimation of the source parameters.[2002 year]: Following to the fundamental consideration on the sound source localization problem in 2001, the extensive simulation makes it clear that the proposed method also enables us to find the directions of arrival. These results have been presented in domestic or international conferences. Less