Data-Dependent Initialization for ECM-Based Blind Geometry Estimation of a Microphone Array Using Reverberant Speech

Recently a method has been proposed to blindly estimate the geometry of an array of distributed microphones using reverberant speech, which relies on estimating the coherence matrix of the reverberation using an iterative expectation conditional-maximization (ECM) approach. Instead of using a data-independent initial estimate of the coherence matrix and a matched beamformer to estimate the initial speech and reverberation power spectral densities, in this paper we propose to use a data-dependent initial estimate of the coherence matrix and a time-varying minimum-power-distortionlessresponse beamformer. Simulation results show that the proposed ECM initialization significantly improves the estimation accuracy of the microphone array geometry and increases the generalizability for microphone arrays of different sizes.