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The problem of blind separation of speech signals in the presence of noise using multiple microphones is addressed. Blind estimation of the acoustic parameters and the individual source signals is carried out by applying the expectation-maximization (EM) algorithm. Two models for the speech signals are used, namely an unknown deterministic signal model and a complex-Gaussian signal model. For the...
Natural conversations are spontaneous exchanges involving two or more people speaking in an intermittent manner. Therefore one expects such conversation to have intervals where some of the speakers are silent. Yet, most (multichannel) audio source separation (MASS) methods consider the sound sources to be continuously emitting on the total duration of the processed mixture. In this paper we propose...
Ad hoc wireless acoustic sensor networks (WASNs) hold great potential for improved performance in speech processing applications, thanks to better coverage and higher diversity of the received signals. We consider a multiple speaker scenario where each of the WASN nodes, an autonomous system comprising of sensing, processing and communicating capabilities, is positioned in the near-field of one of...
Widely linear model has recently been used for signal processing applications due to its ability to achieve better performance than conventional linear filtering for non-circular complex random variables (CRVs) and improper quaternion random variables (QRVs). In this paper, we study the time-domain widely linear quaternion model based minimum variance distortionless response beamformer (WL-QMVDR)...
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