Blind Speech Extraction Combining Generalized MMSE STSA Estimator and ICA-Based Noise and Speech Probability Density Function Estimations

摘要

In this paper, we propose a new blind speech extraction method combining ICA-based dynamic noise estimation and a generalized minimum mean-square-error short-time spectral amplitude estimator of the target speech. To deal with various types of speech signals with different probability density functions (p.d.f.), we also introduce a spectral-subtraction-based speech p.d.f. estimation and provide a theoretical justification of the proposed approach., We conduct an experiment in an actual railway-station environment, and show the improved noise reduction of the proposed method by objective and subjective evaluations.