A new algorithm on multiple unknown source signals estimation and separation in a reverberant space

In many voice-related applications, the presence of echoes and overlapping speech signals can degrade the quality or intelligibility of a desired speech signal to be processed. It is, therefore, important to cancel the echoes and to separate overlapping speech signals from a mixture of these components, so that a specific function of the system, for instance, transmission, speech identification, or recognition, can be accomplished with better performance. However, in many cases we do not know the properties of the communication channel, and sometimes even the number of speech sources is unknown. In this paper, we propose to use a reference signal to determine the channel characteristics. When the estimated channel parameter matrices are obtained, a recurrence formula can then be used to separate various speech signals including their reverberant counterparts. As a finite impulse response (FIR) model is used to describe the observation model of the sources in the reverberant environment, it is not necessary for the processing speech signals to be uncorrelated. Because it involves only simple computation, our approach can be used in online applications. In this paper, we will investigate the validity of our algorithm and compare it with extended fourth-order blind identification (EFOBI). It is found that our method preserves both signal waveforms and their amplitudes even in a noisy environment, whereas EFOBI has not been able to achieve similar performance.