Blind source separation using time-frequency information obtained from wavelet analysis

鸡尾酒会效应是听觉感知中的一个具有挑战性的问题，它涉及到人类在嘈杂的谈话和背景噪音中将注意力集中在单个谈话者身上的特殊听力能力。我们感兴趣的是如何建造一台机器，以令人满意的方式解决鸡尾酒会问题。这个问题被称为盲源分离。解决这一问题的传统方法是独立成分分析。提出了基于连续小波变换的时频信息的盲源分离方法。盲源分离问题分为空间混合问题、时间混合问题和时空混合问题，本研究利用小波分析的方法研究了空间混合问题和最简单的时空混合问题。我们的方法有以下三个优点。首先估计来源的数量。利用震源个数对其他参数进行了较高精度的估计。估计震源与实际震源之间的误差足够小，我们通过数值模拟验证了这些优点。此外，对于空间混合问题，使用两个以上的小波变换，可以在噪声环境中估计模型参数。在最简单的时空混合问题的情况下，考虑了利用到达时间差的源定位问题。

The cocktail party effect is a challenging problem in auditory perception, which deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noise. Our interest is how to build a machine to solve the cocktail party problem in a satisfactory manner. This problem is called blind source separation. The traditional way to solve this problem is an independent component analysis. We have proposed blind source separation methods based on time-frequency information obtained from the continuous wavelet transform. There are three types of blind source separation problems, which are called spatial mixture problem, temporal mixture problem and spatio-temporal mixture problem, respectively.This research project dealt with the spatial mixture problem and the simplest type of the spatio-temporal mixture problem using wavelet analysis. Our methods have the following three merits.1. The number of sources is estimated firstly.2. Using the number of sources, the other parameters are estimated with high accuracy.3. The errors between estimated sources and real sources are small enough.We have checked these merits according to numerical simulations. Moreover, in the case of spatial mixture problem, using more than two wavelet transforms, we can estimate model parameters in a noisy environment. In the case of the simplest type of the spatio-temporal mixture problem, the source location problem using time difference of arrival measurements is considered.