Single-sensor localization of moving acoustic sources using diffusion kernels

Source localization is a common problem in various fields and has applications in both military and civil sectors. Localization of acoustic sources generally requires a few microphones, but it is also possible to use a single microphone and data that was prerecorded in the same environment. Unfortunately, existing single-microphone localization methods are restricted to acoustic sources that have a fixed location. In this paper, we introduce a supervised method for estimating both the location and velocity of a moving acoustic source, using a single microphone based on a manifold learning approach. Simulation results demonstrate the sensitivity of the algorithm to variations in the speed of the sources, resulting in a tradeoff between the accuracy of the estimated location and the accuracy of the estimated direction. In addition, the results demonstrate the sensitivity to variations in direction and frame length of the received signal. The algorithm performs well in reverberant and noisy environments, yet is sensitive to environmental conditions changes.