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Single-sensor localization of moving acoustic sources using diffusion kernels

机译：Single-sensor localization of moving acoustic sources using diffusion kernels

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摘要

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 trade-off between the accuracy of the estimated location and the accuracy of the estimated direction. In addi-tion, 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 environ-mental conditions changes.(c) 2022 Elsevier Ltd. All rights reserved.

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