## 1 Introduction
However, the perceived sound level does continue to change along with the measured sound level, showing that (1) measured sound level is not enough to predict soundscape descriptors such as 'acoustic comfort', and (2) there is a complex relationship between perceived sound level and soundscape descriptors which is mediated by other factors. Subsequent studies have shown that, even with large data sets and several possible acoustic indicators examined, models that are based on objective/measurable metrics under-perform in predicting soundscape assessment when compared to models based on perceptual responses. [PERSON] et al. [16], with a methodology based on smart phone recordings, achieved \(R^{2}=0.21\) with acoustic input factors \(L_{50}\) and \(L_{10}-L_{90}\), whereas the same dataset and model building method achieved \(R^{2}=0.52\) with perceptual input factors overall loudness (OL), visual amenity (VA), traffic (T), voice (V), and birds (B). This indicates that merely examining the acoustic level is not sufficient for predicting the assessed soundscape quality, and that additional objective factors and a more holistic and involved method of characterizing the environment is required. This protocol is trying to extend the scope of objective measurements that are being collected in conjunction with perceptual responses by including other environmental and visual data. These previous studies have generally been limited by one or many of the following factors: limited number or types of locations, limited responses sample size, and no non-acoustic factors, generally limiting the generalizability of their results beyond the investigated locations. The ability to predict the likely soundscape assessment of a space is crucial to implementing the soundscape concept in practical design. Current methods of assessing soundscapes are generally limited to a post-hoc assessment of the existing environment, where users of the space in question are surveyed regarding their experience of the acoustic environment [11; 17]. While this approach has proved useful in identifying the impacts of an existing environment, designers require the ability to predict how a change or proposed design will impact the soundscape of the space. To this end, a model that is built upon measurable or estimate-able quantities of the environment would represent a leap forward in the ability to design soundscapes. Developing soundscape indices is a process that requires consideration of how people perceive, experience, and understand the surrounding sound environment. For the purpose of modeling and comparisons, it is important that such indices are numerical entities and that these quantities are collected consistently across all investigated spaces and soundscapes. Although the soundscape approach taken in this protocol represents a step-change away from existing methods of noise exposure measurements, strong cues particularly in the realm of acoustic measurement methods should be taken from existing standards both to make use of the significant knowledge and experience that has gone into the creation of these standards and to facilitate compatibility between soundscape and traditional measurements. In general, the measurement methods and best practice given in environmental noise standards such as ISO 1996-1:2016 and ISO 1996-2:2017 [18,19] should be followed wherever possible, including the use of standardized acoustic equipment such as standard sound level meters. An European Research Council (ERC) Advanced Grant project is ongoing to develop the proposed \"Soundscape Indices\" (SSID), which adequately reflect levels of human comfort and preference while integrating measurable and observable quantities. The framework proposed for the SSID project is laid out in detail by [PERSON] et al. [3], the first step of which is generating a large-scale and coherent database of the required soundscape characterization data.