Blind source separation of anechoic mixtures in time domain utilizing aggregated microphones

This paper introduces a blind source separation (BSS) algorithm in the time domain based on the amplitude gain difference of two directional microphones located at the same place, namely aggregated microphones. A feature of our approach is to treat the BSS problem of the anechoic mixtures in the time domain. Sparseness approach is one of the attractive methods to solve the problem of the sound separation. If the signal is sparse in the frequency domain, the sources rarely overlap. Under this condition, it is possible to extract each signal using time-frequency binary masks. In this paper, we treat the non-stationary, partially disjoint signals. In other words, most of the signals overlap in the time domain and the frequency domain though there exist some intervals where the sound is disjoint. We firstly show the source separation problem can be described not as a convolutive model but as an instantaneous model in spite of the anechoic mixing when the aggregated microphones are assumed. We then show the necessary conditions and show the algorithm with the experimental results. In this method, we can treat the problem not in the time-frequency domain but in the time domain due to the characteristics of the aggregated microphones. In other words, we can consider the problem not in the complex space but in the real space. The mixing matrix can be directly identified utilizing the observed signals without estimating the intervals where the signal is disjoint through all the processes.