Hearing Protection Devices (HPDs) can protect the ear against loud potentially damaging sounds while allowing lower-level sounds such as speech to be perceived. However, the impact of these devices on the ability to localize sound sources is not well known. To address this question, we propose two different methods: one behavioral and one dealing with acoustical measurements. For the behavioral method, sound localization performance was measured with, and without, HPDs on 20 listeners. Five HPDs, including both passive (non-linear attenuation) and three active (talk-through) systems were evaluated. The results showed a significant increase in localization errors, especially front-back and up-down confusions relative to the “naked ear” test condition for all of the systems tested, especially for the talk-through headphone system. For the acoustic measurement method, Head-Related Transfer Functions (HRTFs) were measured on an artificial head both without, and with the HPDs in place. The effects of the HPDs on the spectral cues for the localization of different sound sources in the horizontal plane were analyzed. Alterations of the Interaural Spectral Difference (ISD) cues were identified, which could explain the observed increase in front-back confusions caused by the talk-through headphone protectors.

This paper presents a new sonar target classification approach based on the use of time-frequency filters. Their design is carried out from the free field response of a reference target, and more precisely from the analysis of echo formation mechanisms in the time-frequency plane. The study of the relevance and the robustness of this approach in approximately real sonar conditions is conducted from experimental measurements in a tank. A data base is set up that contains a large set of target responses in the free field, near different interfaces and in waveguide situations. First, the efficiency of the method for the recognition of a nickel molybdenum spherical shell, corresponding to a class of man made targets whose size is much smaller than the sonar beam (finite size) is shown (100% of recognition). Second, a classification procedure between different targets of finite size is conducted: more than 85% of good classification is obtained (except for the marble solid target). Finally, in the presence of numerical noise, the method is found to be robust even for a low signal to noise ratio.

Fabric noise generated by fabric-to-fabric friction is considered as one of the auditory disturbances that can have an impact on the quality of some textile products. For this reason, an instrument has been developed to analyse this phenomenon. The instrument is designed to simulate the relative movement of a human arm when walking. In order to understand the nature of the relative motion of a human arm, films of the upper half of the human body were taken. These films help to define the parameters required for movement simulation. These parameters are movement trajectory, movement velocity, arm pressure applied on the lateral part of the trunk and the friction area. After creating the instrument, a set of soundtracks related to the noise generated by fabric-to-fabric friction was recorded. The recordings were treated with a specific software to extract the sound parameters and the acoustic imprints of fabric were obtained.

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.