Learning a precedence effect-like weighting function for the generalized cross-correlation framework

Speech source localization in reverberant environments has proved difficult for automated microphone array systems. Because of its nonstationary nature, certain features observable in the reverberant speech signal, such as sudden increases in audio energy, provide cues to indicate time-frequency regions that are particularly useful for audio localization. We exploit-these cues by learning a mapping from reverberated signal spectrograms to localization precision using ridge regression. Using the learned mappings in the generalized cross-correlation framework, we demonstrate improved localization performance. Additionally, the resulting mappings exhibit behavior consistent with the well-known precedence effect from psychoacoustic studies.