GCC-PHAT Re-Imagined - A U-Net Filter for Audio TDOA Peak-Selection

Time-difference-of-arrival (TDOA) estimation from GCC-PHAT is not always as straight forward as finding the maximum peak. This work views the GCC output as an image, with time on the vertical axis and TDOA horizontally, to explore if image-to-image machine learning methods can make a more robust filter. The Structure from Sound Database provides audio recorded with a distributed microphone setup and a moving sound source. The audio was fed to GCC-PHAT without pre-processing, and images were produced for batch processing. The ground truth, the direct-path TDOA, shows a continuous curve through time. The GCC output image has a similar curve, but obscured by noise and not at all times texturally different from the multi-path components. The main approach tested is binary semantic segmentation with a U-Net. A challenge is the extreme class imbalance within the image. Preliminary results indicate that the method is valid to detect curves, yet more work is needed to single out the direct path TDOA with confidence.