Permutation alignment of frequency-domain ICA by the maximization of intra-source envelope correlations

This paper presents a novel method for solving the permutation ambiguity of frequency-domain independent component analysis based on source signal envelope correlation maximization. The proposed method is developed for blind source separation with high sampling frequency and significant spatial aliasing. We propose a method that analyzes the source envelope using a rank-one singular value decomposition (SVD) applied to an initial source magnitude spectrogram obtained by a time difference of arrival (TDoA) based permutation alignment method. The permutation for frequencies with incoherent TDoA are corrected by maximizing the cross-correlation of the SVD analyzed source activation vector and each independent component magnitude envelope. We evaluate the separation quality using real high sampling frequency speech captures and the proposed method is found to improve the separation over the baseline algorithm.