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Towards perceptual soundscape characterization using event detection algorithms.

Abstract : Assessing properties about specific sound sources is important to characterize better the perception of urban sound environments. In order to produce perceptually motivated noise maps, we argue that it is possible to consider the data produced by acoustic sensor networks to gather information about sources of interest and predict their perceptual attributes. To validate this important assumption, this paper reports on a perceptual test on simulated sound scenes for which both perceptual and acoustic source properties are known. Results show that it is indeed feasible to predict perceptual source-specific quantities of interest from recordings, leading to the introduction of two predic-tors of perceptual judgments from acoustic data. The use of those predictors in the new task of automatic soundscape characterization is finally discussed.
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Contributor : Catherine Lavandier <>
Submitted on : Thursday, January 10, 2019 - 2:41:56 PM
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  • HAL Id : hal-01926241, version 1


Félix Gontier, Pierre Aumond, Mathieu Lagrange, Catherine Lavandier, Jean-François Petiot. Towards perceptual soundscape characterization using event detection algorithms.. Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2018), Nov 2018, Surrey, United Kingdom. ⟨hal-01926241⟩



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