Blind Speech Separation in Multiple Environments Using a Frequency Oriented PCA Method for Convolutive Mixtures

This paper reports the results of a comparative study on blind speech separation (BSS) of two types of convolutive mixtures. The separation criterion is based on Frequency Oriented Principal Components Analysis (FOPCA). This method is compared to two other well-known methods: the Degenerate Unmixing Evaluation Technique (DUET) and Convolutive Fast Independent Component Analysis (C-FICA). The efficiency of FOPCA is exploited to derive a BSS algorithm for the under-determined case (more speakers than microphones). The FOPCA method is objectively compared in terms of signal-to-interference ratio (SIR) and the Perceptual Evaluation of Speech Quality (PESQ) criteria and subjectively by the Mean Opinion Score (MOS). Usually, the conventional algorithms in the frequency domain are subject to permutation problems. On the other hand, the proposed algorithm has the attractive feature that this inconvenience usually arising does not occur.