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The aim of this work is to develop a device capable to record multiple audio signals (in our case 4 audio signals from 4 microphones of the area) and transmit the information through a network for acoustic source localization. We briefly discuss the first two versions, then the HRTF (Head Related Transfer Function) version of the acoustic sensors i...
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Citations
... A COUSTIC event detection, classification and localization has gained much attention in the signal processing community in recent years. Since the introduction of the acoustic decay model [1], [2] many studies have been proposed in several fields of applications, namely wildlife environments [3], assisted living [4], gunshot characterization [5], underwater sensors networks [6], smart cities [7], and localization [8], just to name a few examples. ...
The present work proposes a new approach to address the energy-based acoustic localization problem. The proposed approach represents an improved version of evolutionary optimization based on Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of random initialization of elephant population, we exploit particularities of the problem at hand to develop an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point are used to determine the regions in which a source is most likely to be located. Secondly, rather than letting elephants to simply wander around in their search for an update of the source location, we base their motion on a local search scheme which is found on a discrete gradient method. Such a methodology significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show that, in terms of localization accuracy, the proposed approach significantly outperforms the standard EHO one for low noise settings and matches the performance of an existing enhanced version of EHO (EEHO). Nonetheless, the proposed scheme achieves this accuracy with significantly less number of function evaluations, which translates to greatly accelerated convergence in comparison with EHO and EEHO. Finally, it is also worth mentioning that the proposed methodology can be extended to any population-based metaheuristic method (it is not only restricted to EHO), which tackles the localization problem indirectly through distance measurements.
