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Wednesday, July 10 • 10:30 - 13:00
MENTAL REPRESENTATIONS EVOKED BY AIRCRAFT NOISE COMPONENTS

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This study aims at better understanding aircraft noise annoyance through an analysis of mental representations associated to aircraft noise. From a theoretical point of view, we investigate top- down strategies of sound perception. In particular, we want to identify mental representations that are evoked by the sounds and that drive listeners' sound perception. Thanks to interviewing techniques, we helped participants to explicit what they had in mind when listening to the different sounds, and without asking them about annoyance. We focused on three main aircraft noise signal components: multiple pure tones (MPT), blade passing frequency (BPF) and broadband noise (BN). Two sets of sounds corresponding to two types of large aircraft were eared by 84 participants in an isolated sound booth. Each sound was played once, and for each sound participants were asked to answer a series of questions aimed at collecting verbal descriptions on the played sound. Seven different questions were created to help the participants produce a detailed description of his/her perception of the sounds. A content analysis was performed on the verbal data to explicit the relations between the different terms that are present in the verbal data in order to create semantic categories. This method has proven to be efficient since a large and reach verbal corpus has been collected. The linguistic analysis of this corpus revealed specific mental representations of each aircraft noise component. In particular, both MPT and BPF had negative connotations, with different levels and with specific semantic profiles in contrast with BN. Finally, this study leads to new perspectives in the analysis of aircraft noise annoyance since that annoyance is also function of mental representations that are evoked by aircraft noise, and not only by acoustic parameters.


Wednesday July 10, 2019 10:30 - 13:00 EDT
St-Laurent 3, Board 04-A
  T04 Environ. & commun. noise, RS02 Noise impact assess