Improving the Efficiency of DAMAS for Sound Source Localization via Wavelet Compression Computational Grid

TL;DR

This paper introduces a wavelet compression-based method to enhance DAMAS sound source localization efficiency, significantly reducing computation time while maintaining spatial resolution, with minor aliasing issues for complex sources.

Contribution

The novel wavelet compression approach improves DAMAS efficiency without altering the core algorithm, enabling faster sound source localization in industrial applications.

Findings

Efficiency of DAMAS increases with higher compression ratios.

Method significantly reduces runtime in practical scenarios.

Spatial resolution is largely preserved despite compression.

Abstract

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.