Improving the Efficiency of DAMAS for Sound Source Localization via Wavelet Compression Computational Grid
This incremental improvement addresses efficiency issues in industrial acoustic source localization applications, particularly for scenarios with localized sound sources and specific frequency bands.
The paper tackles the high computational cost of the DAMAS deconvolution method for sound source localization by introducing a wavelet compression computational grid, which reduces run time significantly with increasing compression ratios while largely maintaining spatial resolution.
Phased microphone arrays are used widely in the applications for acoustic source localization. Deconvolution approaches such as DAMAS successfully overcome the spatial resolution limit of the conventional delay-and-sum (DAS) beamforming method. However deconvolution approaches require high computational effort compared to conventional DAS beamforming method. This paper presents a novel method that serves to improve the efficiency of DAMAS via wavelet compression computational grid rather than via optimizing DAMAS algorithm. In this method, the efficiency of DAMAS increases with compression ratio. This method can thus save lots of run time in industrial applications for sound source localization, particularly when sound sources are just located in a small extent compared with scanning plane and a band of angular frequency needs to be calculated. In addition, this method largely retains the spatial resolution of DAMAS on original computational grid, although with a minor deficiency that the occurrence probability of aliasing increasing slightly for complicated sound source.