Listening to the Data: Opportunities for Data-driven Innovation in Environmental Acoustics

A core facet of acoustic engineering is the collection of data. Noise and vibration measurements form the basis for the predictions, assessments and designs produced by acoustic engineers. Environmental acoustics often considers the impact of unwanted noise on large scale, requiring information from various sources to be synthesised. However, despite being a data-centric industry, the full potential of data is underutilised. This paper identifies opportunities for the industry to innovate in how it makes use of data. This paper identifies potential innovations in the areas of Geographic Information Systems (GIS), Internet of Things (IoT) and psychoacoustics where data could be used to overcome key challenges being faced in environmental acoustics. An approach to acoustics that embraces data-driven innovations will enable acoustic engineers to deliver better results for the community.

Challenges being faced in environmental acoustics

An increasing demand to quantify and remedy environmental noise

Environmental noise is unwanted sound generated by human activities that is detrimental to human health and quality of life (Murphy & King 2014). Evidence suggests that there is a relationship between environmental noise and both sleep disturbance and cardiovascular disease (Department of Health 2018, p.63). A study by the World Health Organisation has found that “at least one million healthy life years are lost every year from traffic related noise in the western part of Europe” (2011, p. 102). As cities become more urbanised the problem of noise pollution worsens twice over, firstly via an increase in noise sources and secondly as people live closer proximity to these sources. The NSW Environmental Protection Authority indicates that this trend has worsened to the point that over “1.5 million residents in Sydney are exposed to outdoor noise levels which may affect sleep and amenity” (2003). Innovation in the acoustic industry is crucial to combat noise pollution as a public health priority.

As noise levels increase and become more intrusive in an urban environment, residents’ experience greater discomfort and annoyance (Environmental Protection Authority 2003). This generally leads to complaints to the governing authority and an expectation that corrective action will be taken. In addition, as a community’s standard of living increases, they tend to become more sensitive to noise. This phenomenon can be related to Maslow’s hierarchy of needs, whereby as individuals’ more basic physiological needs are met, they look to fulfil higher level needs such as peacefulness and noise amenity (Luz 2008). Therefore, as urban populations become more affluent there is a growing need to respond quickly to obtrusive noise and provide wholistic solutions that do more than just limit the noise level.

Consequently, there has been an increase in environmental regulation related to noise, especially so in Australia (CQUniversity 2019). Legislation and guidelines are being developed and expanded for noise from sources such as road traffic, railways, industry and construction. Acoustic engineers expected to deliver more detailed predictions, quantifying noise levels at more locations and for specific scenarios. Noise monitoring is required to be more immediate and extensive, often responding at short notice to complaints. Moreover, it is now necessary to generate results that are more accessible and interactive. New forms of data related technology are required for the industry to meet these growing demands.

Managing the uncertainties of acoustic results

In tension with an increased demand in the quantity and detail of results is the inherent uncertainty and errors in acoustic measurements and predictions. As the scope of environmental noise predictions has increased so has the opportunity for modelling errors to affect the results (Hepworth 2006). As Hepworth explains many of the prediction methodologies currently in use were developed at a time when calculations were performed manually. This is particularly true for Australia where, for example, the most commonly used methodology for predicting rail noise (Kilde Rep. 130) was written in 1984. Implementing these methodologies in modern 3D modelling software packages can lead to unforeseen data errors and a ‘black box’ scenario in which the user is unsure how the result was calculated. If environmental noise is to be predicted on a large scale the standards and methodologies used must be modernised to suit current advances in technology, such that the results can be verified and relied upon.

Hepworth also identifies a risk of errors in environmental noise predictions due to data simplification and efficiency techniques. As noise models are being built on larger scales it has become necessary to simplify the input data used to build the model and process the data in more efficient ways. If applied correctly these processes can reduce the computational time while maintaining the accuracy of the results. However, care must be taken to ensure that validity of the results is not compromised by oversimplifying the data. An investigation by Hepworth found that the efficiency techniques applied by different modelling software packages produced a large variation in the resultant noise levels, sometimes without a significant improvement in the computing time.

Current innovations in data use and future opportunities

Geographic Information Systems (GIS)

As acoustic predictions and measurements have been undertaken on a larger scale the industry is becoming more reliant on Geographic Information Systems (GIS) to store, analyse and present data. GIS is at the forefront of innovation in the acoustic industry and is being used to transform data to provide insights into noise impacts on communities.

Typical inputs for a GIS-based noise model included features of the natural and built environment and noise sources based on measurements. In China, new inputs are being used such as GPS road traffic data and population distribution (Cai et al. 2019). In this study it was necessary to apply several optimisation algorithms to use the high-resolution geo-spatial data effectively. The results of a model such as this could be used to more accurately quantify the number of residents affected by noise pollution when infrastructure planning.

Klæboe et al. have developed a two-tiered GIS noise model that considers the acoustic noise level and the overall soundscape of the neighbourhood (2006). A factor related to the soundscape is calculated using statistical methods and the two layers merged to create a context sensitive noise map. This map would allow acoustic engineers to predict the level of annoyance due to environmental noise, rather than just the noise level.

Geospatial data has been combined with noise predictions to create a simulation of wind farm noise (Manyoky et al. 2014). Both visualisation and auralisation techniques have been used to create a virtual reality experience which can be used to listen to the noise impacts within a GIS based model. Audio-visual tools such as this can give decision makers a better appreciation of what a development will sound like and help set the expectations of residents.

Internet of Things (IoT): novel approaches to monitoring noise

The Internet of Things (IoT) is growing in significance as more devices in our daily lives become connected via the internet. The IoT and faster rates of mobile data transfer have the potential to revolutionise how noise monitoring is conducted. Traditionally, noise levels are monitored by selecting locations considered representative of the area of interest and installing monitoring devices that are relatively heavy, expensive and energy intensive. An alternative approach would be a sensor network of low-cost monitors installed throughout a community. It has been demonstrated that a simple, inexpensive monitoring system can be used to accurately quantify road traffic noise levels, once an algorithm is applied to correct for inaccuracies due to the microphone (Maroufa et al. 2018).

Monitoring environmental noise by crowdsourcing the measurements using a smartphone app has been trialed by Picaut et al. (2019). The proposed system sends geo-referenced noise measurements made using the phone’s microphone to a spatial database. Studies have demonstrated that noise measurements with smartphones have the potential to be accurate enough for the purposes of environmental noise monitoring (Faber 2017; Kardous & Shaw 2016). For this approach to be practical methods of cleaning the data to remove extraneous noise and handling device errors would need to be developed. Pervasive noise monitoring on a large scale could be used as an input into GIS noise mapping and provide acoustic engineers with a much more complete picture of the noise environment.

Using psychoacoustics and data to deliver solutions

Assessments of environmental noise are typically based on the premise that individuals’ annoyance is related to measured noise level through a dosage effect (Murphy & King 2014). Acoustic engineers aim to reduce the number of annoyed individuals to an acceptable level by achieving a target noise level. However, using advances in technology and psychoacoustics it may be possible to targeting individuals’ perception of noise directly, rather than via a measurement of noise levels.

Psychoacoustics is the study of how sound is perceived. It is not uncommon for there to be a disconnect between the subjective perception of a noise and the objective noise level measured. For example, it has been observed that after planting vegetation between roads and houses, residents will report a significant difference in noise levels despite the actual attenuation of noise being quite small (Huddart 1990). It has been suggested that this effect may be due to reducing the visual impact of the noise source, creating a feeling of satisfaction that action of some kind had been taken or that the rustling of leaves generates a pleasant sound masking effect.

Data collection techniques could be used to determine a community’s attitude towards noise sources and a mitigation strategy designed that specifically targets their perception. This it could be done actively with surveys or tests sent via apps to residents before and after a mitigation strategy is put in place. Alternatively, data could be collected passively, monitoring residents’ activities, sleep patterns and mood. If so, measures would need to be put in place to ensure the privacy of individuals was protected in the process of collecting and storing the data. Traditionally noise mitigation has involved building a noise wall a great expense, sometimes to the dissatisfaction of residents due to shadowing and visual impacts. A new approach that monitors a community’s perception of noise has the potential to be cheaper and provided greater, longer-lasting benefits.

A soundscaping approach involves considering the cumulative effect of all noise sources (both positive and negative) and developing new acoustical parameters directly related to residents’ perception of sound (Schulte-Fortkamp et al. 2007). This is inherently more complicated than measuring the noise level of one source and as such this strategy requires more sophisticated techniques of data collection and analysis. Data analytical methods such as machine learning could be used to relate characteristics in the ambient noise environment to positive or negative perceptions of sound. This could be combined with IoT technology to create a more pleasant sound environment. For example, when an annoying noise is detected outside an apartment the noise level of the appliances inside could be increased to create a subtle masking effect. The acoustic industry could move away from being driven by complaints, which has been demonstrated to be unreliable index (Maziul et al. 2005) and towards creating an optimal sound environment.

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