Successive relative transfer function identification using single microphone speech enhancement

Dani Cherkassky, Shlomo E. Chazan, Jacob Goldberger, Sharon Gannot

Abstract: A distortionless speech extraction in a reverberant environment can be achieved by an application of a beamforming algorithm, provided that the relative transfer functions (RTFs) of the sources and the covariance matrix of the noise are known. In this contribution, we consider the RTF identification challenge in a multi-source scenario. We propose a successive RTF identification (SRI), based on a sole assumption that sources become successively active. The proposed algorithm identifies the RTF of the ith speech source assuming that the RTFs of all other sources in the environment and the power spectral density (PSD) matrix of the noise were previously estimated. The proposed RTF identification algorithm is based on the neural network Mix-Max (NN-MM) single microphone speech enhancement algorithm, followed by a least-squares (LS) system identification method. The proposed RTF estimation algorithm is validated by simulation.