Optimal microphone placement for indoor acoustic localization using evolutionary optimization

Abstract

Optimal placement of sensors is a fundamental task to achieve high performance of different applications, especially in multi-sensor scenarios and complex environments. This paper proposes an alternative for automatically estimating the optimal placement of multiple microphones (individual or conforming arrays), in arbitrary complex indoor environments, for acoustic localization tasks. The proposal is based on evolutionary optimization strategies (genetic algorithms). We also propose a spatial likelihood function based on a recently described acoustic model which is able to accurately predict an acoustic power map for a given sensor and source configuration. The spatial likelihood function is the base to develop error metrics, from which different objective performance measures are derived. An extensive evaluation has been carried out, generating different placement alternatives according to the number of microphones, their arrangement (individual or conforming arrays), and the optimization criteria used in the optimization process. In addition, these results have been compared with the ones obtained by manual and random sensor placement, showing that the proposal clearly outperforms them, thus allowing to validate it.