Self-Supervised Incremental Learning for Sound Source Localization in Complex Indoor Environment

This paper presents an incremental learning framework for mobile robots localizing the human sound source using a microphone array in a complex indoor environment consisting of multiple rooms. In contrast to conventional approaches that leverage direction-of-arrival (DOA) estimation, the framework allows a robot to accumulate training data and improve the performance of the prediction model over time using an incremental learning scheme. Specifically, we use implicit acoustic features obtained from an auto-encoder together with the geometry features from the map for training. A self-supervision process is developed such that the model ranks the priority of rooms to explore and assigns the ground truth label to the collected data, updating the learned model on-the-fly. The framework does not require pre-collected data and can be directly applied to real-world scenarios without any human supervisions or interventions. In experiments, we demonstrate that the prediction accuracy reaches 67% using about 20 training samples and eventually achieves 90% accuracy within 120 samples, surpassing prior classification-based methods with explicit GCC-PHAT features.