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Multi-level Regression Modelling of Dynamic Urban Soundscapes: Predicting Perceptual Assessments
Multi-level Regression Modelling of Dynamic Urban Soundscapes: Predicting Perceptual Assessments
Urban noise pollution affects 80 million EU citizens with substantial impacts on public health which are not well addressed by conventional noise control methods. Traditional noise control methods have typically limited their focus to the reduction of unwanted noise, ignoring the potential benefits of increasing positive sounds and remaining restricted by practical limitations of noise reduction. Modern approaches to achieve improved health outcomes and public satisfaction aim to incorporate a person's perception of an acoustic environment, an approach known as 'Soundscape'.
When attempting to apply soundscape in practical applications in the built environment, it is immediately apparent that a predictive model of the users' perceptual response to the acoustic environment is necessary. Whether to determine the impact of a design change, or to integrate large scale data at neighbourhood and city levels, a mathematical model of the interacting factors will form a vital component of the implementation of the soundscape approach. The soundscape is influenced by several levels of factors: the immediate and long-term acoustic environment, other environmental factors (e.g. temperature, air quality), the physical / visual characteristics of the space, the type of architectural space, and even cultural and country-level expectations. When approached in a predictive model context, the acoustic data must form the core components, but a coherent framework for describing how the influence of the acoustic factors is affected by the non-acoustic factors is required. In order to properly extract the influences of all of these levels of factors as well as to build a generalisable model which can be used in practice, these factors should be combined into a single multi-level model. The first key step for this approach is the creation of a coherent, large-scale, multi-factor database of objective environmental measurements and subjective perceptual responses.
My research makes use of in-person field questionnaires, long-term manned questionnaires, and multi-factor characterization of the environment as part of the ERC-funded project Soundscape Indices (SSID) and in collaboration with The French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR) to collect this database across a wide range of locations and soundscape types.
This approach is unique in that it:
1. fundamentally incorporates all identified factors of soundscape perception in a coherent manner;
2. is extensible and interpretable;
3. considers how soundscapes change over both multi-hour and multi-day timescales and incorporates this dynamic behaviour for increased accuracy.