Identifying and recognizing noticeable sounds from physical measurements and their effect on soundscape

Invited paper

Karlo Filipan

Ghent University

Tuesday 2 june, 2015, 16:20 - 16:40

0.3 Copenhagen (49)

In contrast to the classical noise control, the soundscape approach analyzes the person-environment interaction in more detail including positive as well as negative effects. Environmental sound is often a by-product of the environment and listening to it is rarely the purpose of being in a place. Therefore, noticing and inhibition-of-return play an important role in the theoretical model for people’s perception. The proposed model extends from an initial physiological response to environmental sound over noticing, identifying, and recognizing to appraisal within a context of personal beliefs and expectations. Consequently, it attempts to encompass the whole interaction of the person and the environment from sensory inputs to actions related to the response on the environment. During the recent years, environmental monitoring and sound monitoring as its part have experienced a technology driven growth to which various governing bodies have shown a significant interest. However, the challenge now presents itself in the analysis of the acquired big data especially when it comes to perception. Several aspects of the above mentioned theoretical model for perception of environmental sound have been implemented in the computational models for this purpose. The models are based on the artificial neural network structure that mimics many of the low level neural processes occurring in the human brain. However, the models do not attempt to make a simulation of a complete brain, which is still well out of reach even for the most advanced computer architectures. This contribution will focus in particular on the object formation and attention processes in an attempt to predict which sounds would be noticed by the user of a space and how this will affect the soundscape. Examples from urban parks and residential areas will be shown to illustrate how accurately the model based on physical inputs solely can match the human response.

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