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

基于实际声环境中的统计独立性识别声源位置或方向

基本信息

批准号：
13650472
负责人：
NAKASAKO Noboru
金额：
$ 0.83万
依托单位：
Kinki University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2002
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13650472/
关键词：
MUSIC Method Kalman filter Blind source separation Point source Phase difereace Sound attenuation in distance Estimation of source position or direction Inverse filter 複素ICA 距離減衰音源方位推定音源位置推定信号回復

项目摘要

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

声源定位问题在声学的各种实际领域中一直是重要的，并且已经开发了许多方法，它们中的大多数仅利用观测值的低阶统计量，例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（伊卡）成为解决盲信号分离问题的热门，其中独立的源混合，然后由多个传感器测量。对于观测值在源信号之间具有时间差或受系统（房间等）影响的情况，也提出了复信号的伊卡。在本研究中，从不同的角度提出了一些估计声源位置或方向的尝试。特别地，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年取得的成果如下。[ ...更多信息 2001年]：作为研究的第一步，相关方法被引入到计算在每个麦克风的观测之间的延迟时间，然后估计声音的方向。第二种方法分别采用MUSIC方法和Kalman滤波方法估计声源方向和检测声源幅度信息进行声源定位;第三种方法是基于复伊卡方法估计时延或声源与接收机之间的距离，特别是对于具有时延的混合观测值。此外，我们可以很容易地构造一个逆滤波器分离的（整体）真实的混合信号使用伊卡估计的参数，而通常的复杂伊卡需要综合所有的频率成分。最后，通过基本的仿真实验，验证了该方法的高质量盲分离效果以及对源信号参数估计的有效性。[2002年份]：继2001年对声源定位问题的基本考虑之后，广泛的仿真清楚地表明，所提出的方法也使我们能够找到到达方向。这些成果已在国内或国际会议上发表。少