PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Towards soundscape indices
Jian KANG, Francesco ALETTA, Tin OBERMAN, Mercede ERFANIAN, Magdalena KACHLICKA,
Matteo LIONELLO, Andrew MITCHELLUCL Institute for Environmental Design and Engineering, The Bartlett,University College London, United Kingdom

ABSTRACTThe growing field of soundscape studies considers sound environments as perceived, in context, with aninterdisciplinary approach. This paper outlines an ongoing European Research Council (ERC) Advanced
Grant project, which aims to establish “soundscape indices” (SSID). By taking psychological,(psycho)acoustical, neural and physiological, and contextual factors into account, SSID will adequatelyreflect levels of human comfort to integrate side-by-side with (and eventually replace) decibel-based metrics
into existing (international) regulations, shifting the focus from noise control to a more holistic approach.
Steps to achieve this include: to characterise soundscapes, by capturing acoustic environments andestablishing a comprehensive database; to identify key factors and their influence on soundscape quality
based on the database, by conducting laboratory psychological evaluations, acoustical/psychoacoustic factors
analysis, and also, to research the neural and psychophysiological underpinnings of soundscape experience,by applying techniques such as functional Magnetic Resonance Imaging (fMRI) and Skin Conductance
Response (SCR); to develop, test and validate the soundscape indices, by analysing the influences of various
factors; to demonstrate the applicability of the soundscape indices in practice, by establishing frameworks
for soundscape prediction, design, and standardisation. Ultimately, the findings of SSID will allow for aneasy assessment of public spaces and the increase of the noise management impact.
Keywords: Soundscape database, Neural and physiological, Contextual factors

1. INTRODUCTION
For environmental sound quality, it is widely recognised that reducing the sound level is not always
feasible or cost-effective. More importantly, with only ~30% of noise annoyance depending on
physical aspects, such as acoustic energy, sound level reduction will not necessarily lead to improved
quality of life (1). Soundscape research represents a paradigm shift in that it combines physical, socialand psychological approaches and considers environmental sounds as a “resource” rather than a“waste”, by relating to perceptual constructs rather than just physical phenomena (2–4).
The decibel (dB) is the most commonly used index to evaluate environmental sound quality, butthere have been numerous criticisms on its effectiveness, as the correlations between dB and perceived
sound quality (e.g. noise annoyance) are often low (2). Psychoacoustic parameters, including loudness,fluctuation strength, roughness, sharpness, and pitch strength, which are effective for evaluating thesound quality of industrial products, have rather limited applicability in environmental acoustics (5)where a significant feature is that there are multiple and dynamic sound sources. While it has been
demonstrated that people perceive/experience acoustic environments differently, there must be
fundamental
factorswhich
determine
theirperceptions,
including
psychological,
acoustic/psychoacoustic, neural and physiological, and contextual factors.
This paper outlines an ongoing European Research Council (ERC) Advanced Grant project, whichaims to establish “soundscape indices” (SSID), adequately reflecting levels of human comfort while
integrating decibel-based metrics. It first presents a framework of developing SSID, including
relations among soundscape descriptors, correction factors, and indicators, as w ell as the modelling
process. It then describes a protocol for capturing acoustic environments and consequently
establishing a comprehensive database to characterise soundscapes. This is followed by a descriptionof the main research themes that are being pursued to identify key factors and their influence onsoundscape quality based on the database. Finally, the SSID development and application are
discussed.

2488

2. FRAMEWORK FOR SOUNDSCAPE INDICES DEVELOPMENT
There is an ongoing process of methodological standardization in the soundscape community. The
urgent need for solutions to overcome (or rather integrate) noise control engineering approaches inenvironmental management policies is prompting increasing efforts in both soundscape research andpractice to come up with operational tools that can be used to assess and anticipate how urban acoustic
environments will be perceived. These include soundscape descriptors, indicators, and indices, whichhave been acknowledged as important topics to address. Aletta et al. (6) proposed a definition ofsoundscape descriptors and indicators, where the former are “measures of how people perceive theacoustic environment”, whilst the latter are “measures used to predict the value of a soundscapedescriptor”. Taking a further step, soundscape indices can then be defined as single-value scales
derived from either indicators or descriptors that allow for comparisons across soundscapes. This
process is schematically described in Figure 1.
Developing soundscape indices is a process that requires consideration of how people perceive,
experience and understand the surrounding sound environment (7). For the purpose of modelling andcomparisons, it is important that such indices are numerical entities. Aletta et al. (6) have highlighted
that the framework for developing soundscape indices could be organised into a three -step process
based on: (a) collecting soundscape data; (b) characterising the (acoustic) environment; and (c)modelling.
Collecting soundscape data relates to quantifying soundscape descriptors: this process can be
carried out on site, in simulated and/or reproduced environments, or via recalling the experience inmemory. Characterising the acoustic environment relates to physical entities of sound phenomena andrelies on established (psycho)acoustic metrics, such as equivalent sound level, loudness, and otherrelated parameters. There is a growing consensus that other non -acoustic factors (e.g., vision, smell,etc.) should also be considered in this context (8, 9). The third step relates to modelling therelationship between descriptors and indicators. This will, in turn, pave the way for the definition of
the soundscape indices that will be used as single-value representations of the soundscape quality ofa place.
Overall, soundscape indices are important to implement the soundscape approach in urban planningand design, with the ultimate goal of creating spaces of high acoustic quality. For this to happen, it istherefore required that the soundscape international community makes more efforts to identify and toagree on relevant soundscape descriptors and corresponding affecting factors.

Figure 1 – A visual schematization of the overall framework for developing soundscape indices2489

3. SOUNDSCAPE DATABASE
3.1 Protocol for On-site Data Collection
In order to build a useful soundscape database for the prediction model shown in Figure 1 and tocover a reasonable variety of auditory and non-auditory factors, a number of sites around the world
are being surveyed in the context of the SSID project. A protocol for data collection has been
established to gather comparable soundscape-related parameters at each site. It comprises soundscapedescriptors but also a number of other parameters which are speculated to influence human perceptionof the acoustic environment. The protocol gathers subjective and objective data, all quantified to serve
as soundscape descriptors and indicators. The protocol is being applied on-site following themeasurement-point approach, where all the metrics are related to a delimited area, i.e. a measurement
point in an urban open space.
The subjective responses are being collected on-site in two stages: via questionnaires handed out
to the untrained participants and via researcher’s notes. The survey questionnaire is based on Method
A of the ISO/TS 12913-2:2018 (10) and the WHO-5 Well-Being Index (11) and it comprises soundsource identification, soundscape descriptors, assessment of the overall environment, participants’demographic and socio-economic data, and self-reported well-being. The researcher’s notes describe
dominant sound events observed by the researchers and comments on participants’ activities prior andduring the survey (e.g., was a participant part of a group, was he/she passing by or staying in themeasurement-point vicinity?).
The objective measurements are being collected in three stages:- representative spatial, audio-visual recording directly preceding the collection of subjectivedata;- reference binaural recording during the subjective data collection;- continuous ‘monitoring-type’ monaural audio and environmental data logging during the wholesurvey session (during both representative and reference recordings).
The audio-visual recording and environmental data logging are performed using the 360-camera(for spatial and reference visual data), a sound level meter, a 1st order ambisonic microphone with theaccompanying portable audio recorder, binaural recording system and an environmental meter(measuring light, humidity, and temperature amongst other parameters). The setup is conceived tocapture both industry-standard monaural recording needed for calculating environmental soundpressure level and psychoacoustic parameters (12) and multichannel recording needed to assess staticand dynamic spatial characteristics, i.e. movement, localisation, etc.
The representative spatial recording will serve as the basis for the reproduction of a VR simulationin laboratory conditions and for testing physiological responses. It is performed to capture a consistentand representative five-minute period.

Figure 2 – The research team conducting a survey at Byng Place/Torrington Square in London in March2019
3.2 Case Study Sites
Over 40 case study sites have been selected in cities all over the world to provide a variety in theauditory and non-auditory (physical-contextual and social-contextual) factors. While a majority of2490

sites providing physical-contextual (visual and morphological) and auditory diversity have been
identified in London, being the project’s base location, a number of sites are being surveyed in China,
Netherlands, Spain, Italy, Croatia, and France. The sites include different urban scales, uses, activities,
and ambiances – from pocket squares in residential parts of Shenzhen and communal gardens in
London to vast monumental squares in Harbin and Shenyang; from an anonymous residential street
in Granada to the iconic Piazza San Marco in Venice. All physical-contextual factors are being
measured from a single point of view, using the data available from the 360-camera and theenvironmental meter.
The meaning embedded in the observed stimuli (qualitative data) has been one of the trademarksof the soundscape approach (13). This can be applied to both auditory and non-auditory factors and isreflected in keeping track of culturally conditioned soundmarks (music, bells, and signals) andlandmarks (architectural setting distinguishable as traditional, historical, international or neutral) .
Auditory factors considered for site selection are based on sound source types (human, natural andsounds). They are being characterised regarding their level, dominance in the overall acoustic
environment, and meaning.
The main physical-contextual factors considered are: the amount of the visible vegetation within
the view-field, openness (defined either as the amount of the visible sky or visible horizon), spatialcomplexity (featuring topography and the sky vs ground ratio), the amount of visible water surface
and the layer of ‘urban and architectural meaning’ (number of items identified as historical/traditional
or international). Physical-contextual factors have a major influence on soundscape descriptors,
especially the visually related ones (9, 14). Probably the crucial factor for determining vibrancy is thepresence of people (8, 9). It can be measured using the video data, either as the number of visible
people or as the area covered by people. Although being a social factor per se, here it is considered apart of the physical context as it is being measured using visual data.
Social-contextual factors are considered to be influenced by sensorial sensitivity, behaviouralexpectations, and intentions, all expected to be related to participant’s cultural background (2, 13),which is the main reason for sampling sites across the world.
Table 1 –Auditory and non-auditory factors observed for site selection
Auditory factors

Non-auditory factors
Physical-contextual factors

Social-contextual factors

Sound source type (dominance

Visual (built environment andSocio-economic andin the acoustic environment)social presence)demographic (status, gender)

Environmental (illuminance,

Pre-conception and personaltemperature, humidity, wind

preference (cultural

speed)

background)

Sound source meaning(soundmark, music, signal)

Visual meaning (cultural
heritage, signalisation)

Intention (activities)

4. EFFECTS OF PSYCHOLOGICAL, ACOUSTICAL/PSYCHOACOUSTICAL AND
NEURAL AND PHYSIOLOGICAL FACTORS
4.1 Acoustic/Psychoacoustic Metrics and Non-Auditory Factors
The relationship between physical attributes of the environment and the perception of the sound asassessed by the people exposed to the environment will be crucial to any proposed soundscape index.
Previous work on identifying the key acoustic and psychoacoustic parameters has so far yieldedconclusive results only in the realm of soundscape identification (15) and has indicated that traditional
acoustic parameters and analysis are insufficient metrics for subjective assessment of a soundscape’s
overall pleasantness (16). However, progress has been made in determining the relationships between(psycho)acoustic parameters and more targeted perceptual attributes such as vibrancy and2491

eventfulness (9, 17).
Although some research has indicated that aspects of the temporal structure of the acoustic
environment may be related to attributes such as “chaotic” or “boring”, the current methods forevaluating the influence of the temporal structure on soundscape perception are lacking and struggle
to generalise soundscape assessments over longer timescales. This study will apply modern advancesin time series analysis and regression computing toward the development of a metric describing thecomplex temporal structure of measured sound environments. This temporal metric will enable us toinvestigate the relationship between medium- and long-timescale patterns in the acoustic environmentand components of the assessments of the soundscape. Further regression analysis can contribute toimproved forecasting of the behaviour and characteristics of acoustic environments, allowing for
extrapolations of the expected soundscape quality based on relatively short measurements.
4.2 Perceptual Factors and Target Population Assessment Simulation
Rating of perceptual attributes is a strategy commonly used in soundscape research. One proposed
method uses a trigonometrical projection over the mean or the median of the distribution of perceptual
attributes (10, 18). However, some observations (19) show that cross-distributions of ratings acrosspaired attributes show patterns that could possibly hide further features to complement or to extend
the results. Considering the current direction of the state of the art, a pilot study (19) has focused on
the scaling deviances across ratings of pairs of bipolar attributes. The information from the bivariate
distribution of these attributes has been examined by means of clustering analysis across multiple
combinations of features. The results will be used for feature selection to understand the mostinfluential combination of features to describe differences between soundscapes which shareparticular properties. These features will be used to highlight psychoacoustic, physical and contextual
factors, which possibly lay behind distribution patterns of ratings between paired bipolar attributes.
This information will be further considered in order to choose the best model strategy to be
implemented in the SSID framework (see section 3.1).
A further important point is to understand the perceptual differences in the assessment between
untrained and soundscape-expert subjects. A study to map the differences between these twocategories (i.e., trained vs. untrained) of subjects will also be taken into account to optimise resourcesin terms of the number of subjects and amount of time needed. This step aims to predict the responses
in a large scale of casual users by only using the assessment of a few trained people. On the other
hand, the prediction of general subjects’ assessment can also be implemented and estimated by using
a dataset augmentation technique by means of Generative Adve rsarial Networks (20). This strategy
makes it possible to automatically fill hundreds of surveys, given particular contextual,
psychoacoustic and environmental parameters/measurements, by simulating a target population which
can be characterised by age, education, socio-economical distribution, and gender proportions.
4.3 Psychophysiological substrates of soundscape
The impact of the acoustic environment on humans’ nervous system functionality and structure,
manifesting as psychological and physical health issues has been long established (21, 22). However,
the explicit peripheral and central psychophysiological mechanisms underlying this impact are far
from understood. The psychophysiology behind soundscape may be described as unbalancing the stateof equilibrium or homeostasis, causing altered behaviours, cognition, and emotions (23, 24). It istherefore important to shed light on the impact of sounds on the human nervous system at theunconscious level by quantifying the physiological and neurophysiological responses such as heart
rate (HR), Skin Conductance Response (SCR), and spectral bands/electrical activity of the brain.
The quantification of the physiological and neurophysiological basis of soundscape, taking into
account the physical properties of the acoustic environment in a similar context, will be valuable inthe development of a robust groundwork for determining why individuals perceive acoustic
environment the way that they do, and in the prediction of the perceptual appraisals.
The SSID project aims at measuring HR and SCR in response to spatially recorded sounds,
representing three main categories/sound sources, namely nature, mechanical, and human, in order toextrapolate physiological patterns that arise from those sound sources. Additionally, the electricalactivity of the brain will be probed in response to sounds from single vs. complex sources, back - vs.
foreground sounds, and short vs. long sounds, respectively, for the purpose of taking one clos er step
toward characterising soundscape at the brain level. Overall, understanding the perception of the2492

acoustic environment at the unconscious level may create a beneficial foundation for
psychophysiological modelling of soundscape and the characterization of soundscape regarding thetype of evoked responses. Such results will form a key element in the model for soundscape indices ,determined by physiological and neurophysiological considerations.
4.4 Neural correlates of soundscape experience
Based on the results from complex acoustic analyses of the acoustic environment recordings it ispossible to attempt predictions about people’s perception of sounds present in the environment (25),but yet very little is known from a neuroanatomical perspective about why those predictions might betrue. Many psychoacoustic parameters, such as loudness, pitch, timbre, and sound location, dependon complex spectral and temporal aspects of sounds, and so result in multidimensional perceptual
sensations (26–28). It is important to understand how the relationships between physical properties of
acoustic stimuli, neural processing, and their connection with perceptual attributes may be reflectedin brain processes and how subjective perception might be shaped by contextual m odulation ofauditory connectivity. The use of functional magnetic resonance (fMRI) offers an opportunity to study
the relationship between physical stimulus properties, stimulus representation in the brain, and theirconnection with perceptual attributes more directly by identifying patterns of activation in response
to selected sound samples. Since neural responses are not correlated with a listener’s perceptual
reports until the level of the cortex (29), it is expected that modulations of the cortical activity can belinked to the subjectively determined state of soundscape quality. Therefore, the magnitude and extent
of the cortical activation can be described as a function of physical sound properties and equivalent
perceptual attributes to provide insights into the relationship between fMRI activation, thecharacteristics of the stimulus, and its perceptual outcome (i.e., the soundscape).
In order to analyse the acoustic scene, our auditory system needs to be able to extract certain
stimulus attributes while generalising across other properties. The effectiveness of those processes
and the ability to listen selectively in complex and noisy environments is crucial to our everyday
behaviour and effective communication. Since the process of selectively dire cting attention to a single
auditory stream in a complex multisensory scene may shape our perceptual organisation of theelements present in this scene, attention might be one factor crucial for understanding those processes(30). It is thus important to explain how human attention is drawn, and what are the consequences ofthe sustained attention or lack of it, in a dynamic process of perceiving the acoustic environment.
Another approach employed in this study will be the use of virtual reality and eye-tracking
technologies which would help to understand the interactions between the auditory and visual signal
processing (31, 32) as well as to tackle the role of saliency, feature characteristics, and goal-directedbehaviour in shaping attention distribution. By manipulating sound samples and visual features
present in the virtual environment, it is possible to extract the information about the existing crossmodal audio-visual effects and attentional effects driven by different attributes and their influence onsoundscape.
Understanding neural mechanisms behind sound perception will feed directly into the proposedsoundscape indices by explaining the relationship between physical attributes of the environment andthe perception of the sound as assessed by the people exposed to this environment and providing
neuroanatomical validation of the results yielded by the current research.

5. SOUNDSCAPE INDICES DEVELOPMENT AND APPLICABILITY
Based on the statistical examination of the interrelationships among factors in different facets,
formula(s) and/or model(s) for soundscape indices can be derived/constructed. The soundscapeindices may take the form of a single index or a set of indices (also see Figure 1). For the former, it
could be SSID = f(physical factors) + f(contextual factors) + …, with corrections by sociodemographical factors and modifications with psychological, neural, and physiological considerations.
SSID could be a single numerical indicator or a fuzzy indicator of possibilities. The SSID could also
be calculated with a computer model, rather than an analytical/empirical formula, if there are
multiple/complex correlations among the determining factors, where artificial intelligence (AI) or
machine learning (ML) techniques could also be considered (33) For the latter, the SSID will be basedon a set of formulas or computer models, reflecting multiple attributes (e.g. loudness, calmness, soundpreference, vibrancy), which could also be regarded as intermediate indices.

2493

The SSID project is also addressing soundscape prediction and design. With the above SSID
models, soundscape prediction tools could be established, with SSID as the key output. The inputs, as
designable factors, could include various sound sources and their spatial and temporal conditions forpredicting sound levels, people movements and spatial distribution, and 3D acoustic animation of
scenarios. Such tools will lead to the future generation of soundscape mapping, going beyo nd thecurrent noise mapping. The development of SSID will provide the foundations for relevant standards
and policies in sound environment design and regulation.

6. CONCLUDING REMARKSIn this paper, the framework and basic steps of developing soundscape indices have been discussed.
It is expected that this will enhance the underpinning science of soundscape studies, by fosteringinterdisciplinary cross-breeding of emerging scientific ideas. The soundscape indices will support theimplementation of soundscapes - by integrating into planning policies to better inform the
management and planning of acoustic environments, allowing for better tailored improvements todesign the built environment, contributing to creating healthier, more enjoyable and liveable
environments, with respect to the planning of new living and recreational areas and to the reshapingof unsustainable older developments.

ACKNOWLEDGEMENTS
This project has received funding from the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation programme (grant agreement No. 740696, project title:soundscape indices - SSID).

REFERENCES
1. Guski R. Psychological methods for evaluating sound quality and assessing acoustic information.
Acta Acustica united with Acustica. 1997; 83: p. 765-774.
2. Kang J. Urban Sound Environment London: Taylor & Francis incorporating Spon; 2007.
3. Kang J, Schulte-Fortkamp B, editors. Soundscape and the Built Environment Boca Raton: CRC
Press; 2015.
4. Kang J. From dBA to soundscape indices: Managing our sound environment. Frontiers ofEngineering Management. 2017; 4(2): p. 184-192.
5. Fastl H, Zwicker E. Psychoacoustics - Facts and Models Berlin: Springer Verlag; 1990.
6. Aletta F, Kang J, Axelsson Ö. Soundscape descriptors and a conceptual framework for developing
predictive soundscape models. Landscape and Urban Planning. 2016; 149: p. 65-74.
7. International Organization for Standardization. ISO 12913-1:2014 Acoustics — Soundscape — Part
1: Definition and conceptual framework. Geneva; 2014.
8. Aletta F, Kang J. Descriptors and indicators for soundscape design: vibrancy as an example. In
Proceedings of the Internoise 2016 Conference; 2016; Hamburg.
9. Aletta F, Kang J. Towards an Urban Vibrancy Model: A Soundscape Approach. International Journalof Environmental Research and Public Health. 2018; 15(8): p. 1712.
10. International Organization for Standardization. ISO/TS 12913-2:2018 Acoustics — Soundscape —Part 2: Data collection and reporting requirements. Geneva; 2018.
11. World Health Organization. Environmental Noise Guidelines for the European Region. Copenhagen;2018.
12. International Organization for Standardization. ISO 1996-1:2016 Acoustics — Description,measurement and assessment of environmental noise — Part 1: Basic quantities and assessment
procedures. Geneva; 1996.
13. Schafer RM. The tuning of the world New York: Knopf; 1977.
14. Hong JY, Jeon JY. Influence of urban contexts on soundscape perceptions: A structural equation
modeling approach. Landscape and Urban Planning. 2015; 141: p. 78-87.
15. Rychtáriková M, Vermeir G. Soundscape categorization on the basis of objective acousticalparameters. Applied Acoustics. 2013: p. 240-247.
16. Aletta F, Axelsson Ö, Kang J. Towards acoustic indicators for soundscape design. In Proceedings ofthe Forum Acusticum 2014 Conference; 2014; Krakow.
17. Jeon JY, Lee PJ, Hong JY, Cabrera D. Non-auditory factors affecting urban soundscape evaluation.
Journal of the Acoustical Society of America. 2011; 130(6): p. 3761-3770.

2494

18. Axelsson Ö, Nilsson M, Berglund B. A principal components model of soundscape perception.
Journal of the Acoustical Society of America. 2010; 128(5): p. 2836-2846.
19. Lionello M, Aletta F, Kang J. On the dimension and scaling analysis of soundscape assessment tools:a case study about the "Method A" of ISO/TS 12913-2:2018. In Proceedings of the International
Conference on Acoustics ICA 2019; 2019; Aachen.
20. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative
adversarial nets. In NIPS'14 Proceedings of the 27th International Conference on Neural Information
Processing Systems; 2014; Montreal. p. 2672-2680.
21. Kliuchko M, Heinonen-Guzejev M, Vuust P, Tervaniemi M, Brattico E. A window into the brain
mechanisms associated with noise sensitivity. Scientific reports. Scientific Reports. 2016; 6: p.
39236.
22. Kliuchko M, Puoliväli T, Heinonen-Guzejev M, Tervaniemi M, Toiviainen P, Sams M, et al.
Neuroanatomical substrate of noise sensitivity. Neuroimage. 2018; 167: p. 309-315.
23. Azrin NH. Some effects of noise on human behavior. Journal of Experimental Analysis of Behavior.
1958; 1(2): p. 183-200.
24. Stansfeld SA, Berglund B, Clark C, Lopez-Barrio I, Fischer P, Öhrström HMM, et al. Aircraft androad traffic noise and children's cognition and health: a cross-national study. The Lancet. 2005;
365(9475): p. 1942-1949.
25. Lunden P, Axelsson Ö, Hurtig M. On urban soundscape mapping: A computer can predict theoutcome of soundscape assessments. In Proceedings of the Internoise 2016 Conference; 2016;
Hamburg.
26. Schreiner CE, Malone BJ. Representation of loudness in the auditory cortex. In Celesia G. HickockG, editors. Handbook of Clinical Neurology, The Human Auditory System. Amsterdam: Elsevier;2015.
27. Arnal LH, Poeppel D, Giraud AL. Temporal coding in the auditory cortex. In Celesia G, Hickock G,
editors. Handbook of Clinical Neurology, The Human Auditory System. Amsterdam: Elsevier; 2015.
28. Walker KMM, Bizley JK, King JK, Schnupp JWH. Cortical encoding of pitch: Recent results andopen questions. Hearing Research. 2011; 271: p. 74-87.
29. Bizley JK, Cohen YE. The what, where and how of auditory-object perception. Nature Reviews
Neuroscience. 2013; 14: p. 693-707.
30. Kondo HM, van Loon AM, Kawahar JIMBCJ. Auditory and visual scene analysis: An overview.
Philosophical Transactions of the Royal Society B. 2017; 372: p. 20160099.
31. Malpica S, Serrano A, Allue M, Bedia MG, Masia B. Crossmodal perception in virtual reality.
Multimedia Tools and Applications. 2019. https://doi.org/10.1007/s11042-019-7331-z
32. Asakura T, Tsujimura S, Yonemura M, Hyojin L, Sakamoto S. Effect of immersive visual stimuli on
the subjective evaluation of the loudness and annoyance of sound environments in urban cities.
Applied Acoustics. 2019; 143: pp. 141-150.
33. Yu L, Kang J. Modeling subjective evaluation of soundscape quality in urban open spaces - An
artificial neural network approach. Journal of the Acoustical Society of America. 2009; 126: p. 11631174.

2495