Auditory perception is an essential part of a robotic system in Human-Robot Interaction (HRI), and creating an artificial auditory perception system that is on par with human has been a long-standing goal for researchers. In fact, this is a challenging research topic, because in typical HRI scenarios the audio signal is often corrupted by the robot ego noise, other background noise and overlapping voices. The traditional approaches based on signal processing seek analytical solutions according to the physical law of sound propagation as well as assumptions about the signal, noise and environments. However, such approaches either assume over-simplified conditions, or create sophisticated models that do not generalize well in real situations.
This thesis introduces an alternative methodology to auditory perception in robotics by using deep learning techniques. It includes a group of novel deep learning-based approaches addressing sound source localization, speech/non-speech classification, and speaker re-identification. The deep learning-based approaches rely on neural network models that learn directly from the data without making many assumptions. They are shown by experiments with real robots to outperform the traditional methods in complex environments, where there are multiple speakers, interfering noises and no a priori knowledge about the number of sources.
In addition, this thesis addresses the issue of high cost of data collection which arises with learning-based approaches. Domain adaptation and data augmentation methods are proposed to exploit simulated data and weakly-labeled real data, so that the effort for data collection is minimized. Overall, this thesis suggests a practical and robust solution for auditory perception in robotics in the wild.