A Supervised Learning Approach to Monaural Segregation of Reverberant Speech

Room reverberation degrades speech signals and poses a major challenge to current monaural speech segregation systems. Previous research relies on inverse filtering as a front-end for partially restoring the harmonicity of the reverberant signal. We show that the inverse filtering approach is sensitive to different room configurations, hence undesirable in general reverberation conditions. We propose a supervised learning approach to map a set of harmonic features into a pitch based grouping cue for each time-frequency (T-F) unit. We use a speech segregation method to estimate an ideal binary T-F mask which retains the reverberant mixture in a local T-F unit if and only if the energy of target is stronger than interference energy. Results show that our approach improves the segregation performance considerably.