High-efficiency sound source localization using data-driven sparse sampling with validation using monopole laser sound source

Abstract

This study proposes a framework that reduces the calculation cost of sound source localization with the Amiet model, using a data-driven sparse sampling method. This method accelerates the calculation of the steering vector used in conventional beamforming. An aeroacoustic wind tunnel test was conducted in a 2 × 2 m2 low-speed wind tunnel, and the proposed method was verified. During the test, a monopole laser sound source, which does not interfere with the flow, was used, and its acoustic signals were measured using a microphone array. Next, steering vectors were reconstructed by discovering dominant modes and optimized sampling points from the training data based on the Amiet model and the modified data-driven sparse sampling method. Finally, the sound-source positions when the steering vector of the proposed model was used were compared with the positions observed when the steering vector of which all the grid points were calculated was used. The error was less than 2 mm when 16 modes were used, and the calculation time was reduced to ∼1/33 of that of the previous Amiet model.