Robust interchannel phase difference modeling with wrapped regression splines

Inter-channel phase differences (IPD) are commonly used as features in microphone array-based source separation and localization algorithms. However, under real-world conditions, we may observe significant deviations from the assumed anechoic, linear model. Non-linearities arise from spatial aliasing, reverberation, and poor array calibration. In this work, we view the features as comprising a circular-linear dataset and model them with a wrapped regression spline. Experimental results demonstrate that this approach leads to more robust speech denoising than a linear model.