Sound localization models as evaluation tools for tactical communication and protective systems

Tactical Communication and Protective Systems (TCAPS) are hearing protection devices that sufficiently protect the listener's ears from hazardous sounds and preserve speech intelligibility. However, previous studies demonstrated that TCAPS still deteriorate the listener's situational awareness, in particular, the ability to locate sound sources. On the horizontal plane, this is mainly explained by the degradation of the acoustical cues normally preventing the listener from making front-back confusions. As part of TCAPS development and assessment, a method predicting the TCAPS-induced degradation of the sound localization capability based on electroacoustic measurements would be more suitable than time-consuming behavioral experiments. In this context, the present paper investigates two methods based on Head-Related Transfer Functions (HRTFs): a template-matching model and a three-layer neural network. They are optimized to fit human sound source identification performance in open ear condition. The methods are applied to HRTFs measured with six TCAPS, providing identification probabilities. They are compared with the results of a behavioral experiment, conducted with the same protectors, and which ranks the TCAPS by type. The neural network predicts realistic performances with earplugs, but overestimates errors with earmuffs. The template-matching model predicts human performance well, except for two particular TCAPS.