Participatory soundscape sensing

TL;DR: The calibration method for sound pressure levels (SPL) measured by mobile phone is described, the PSS’s data temporal-spatial distribution characteristics are analyzed, and the impact of the participants’ age and gender on the data quality is discussed.

Abstract: Soundscape research offers new ways to explore the acoustic environment and potentially address challenges. A comprehensive understanding of soundscape characteristics and quality requires efficient data collection and analysis methods. This paper describes Participatory Soundscape Sensing (PSS), a worldwide soundscape investigation and evaluation project. We describe the calibration method for sound pressure levels (SPL) measured by mobile phone, analyze the PSS’s data temporal-spatial distribution characteristics, and discuss the impact of the participants’ age and gender on the data quality. Furthermore, we analyze the sound comfort level relationships with each class of land use, sound sources, subjective evaluation, sound level, sound harmoniousness, gender, and age using over a year of shared data. The results suggest that PSS has distinct advantages in enhancing the amount and coverage of soundscape data. The PSS data distribution is closely related to the temporal pattern of the human work-rest schedule, population density, and the level of cyber-infrastructure. Adults (19–40 years old) are higher-quality data providers, and women exhibit better performance with respect to data integrity than men. Increasing the proportion of natural source sounds and reducing the proportion of human-made sources of sound is expected to enhance the sound comfort level. A higher proportion of sound harmoniousness leads to higher sound comfort, and the higher proportion of subjective evaluation sound level does not lead to decreased sound comfort. We suggest that the crowdsourcing data with participatory sensing will provide a new perspective in soundscape investigation, evaluation, and planning.

Summary

1. Introduction

• Participatory sensing (PS) is the process through which individuals and communities use the capabilities of mobile devices and cloud services to collect, analyze, and contribute sensory information (Estrin et al., 2010; Burke et al., 2006) .
• Using the concept of PS, sound-recording and noise-monitoring mobile applications and online web survey software have been reported.
• Noteworthy is that some mobile phones' accuracy for measuring noise pollution has been tested (Aumond et al., 2017) , but few of them may be appropriate for noise measurement (Kardous & Shaw, 2014) .
• Additionally, the quality and characteristics of these crowdsourced data lack detailed descriptions or discussion.

2.3. Data quality analysis

• If there are 50 GPS records in 100 measurement activities, the integrity of GPS indicators is 50%.
• In addition, the authors analyzed the sound comfort level relationships with each class of land use, sound sources, subjective evaluation sound level, sound harmoniousness, gender, and age.

3.1. SPL data validation

• During the study period, the authors received observations from 470 model types belonging to 45 mobile phone manufacturers.
• Certain models that the authors have were calibrated, while others can be calibrated in a similar manner.
• Fig. 3 shows that these mobile phones have good correlation with SPM.
• Table 1 shows the average error between each of the mobile phones and SPM is 0.3 dBA (HTC Desire), 0.8 dBA (HTC Wildfire), 1.2 dBA (HTC Incredible), and 0.7 dBA (SAMSUNG I9000), meaning that the calibrated mobile phones are suitable for measuring SPL.

3.2. Data temporal-spatial distribution 8

• Numerous populations, ubiquitous networks, and plentiful numbers of mobile application markets make the measurement sites in China and USA much more numerous than in other locations.
• Gender and age, are uneven in the differences among participants' demographic biases.
• Fig. 6 shows the record integrity for participants under 12 years old was much lower than that of other age groups.
• Women show better performance in data integrity (completing the recording) than men.
• The accuracy of GPS is easily affected by the surroundings, but most distances (81.5%) are less than 50 meters.

3.4. Sound comfort evaluation

• In addition, the authors find that, when the sound comfort level is shifted from very comfortable to very uncomfortable, the ratio of participants that are women and older than 60 years continuously increases.
• The results show that elderly people and women may be more easily negatively affected by environmental noise.

Figures

Fig. 8. Percentage of sound sources and land uses at different sound comfort levels 214 Most of the measurement activities were conducted in a residential area (R). The categories 215 of business area (B), industrial area (M), and road, street, and transportation area (S) have 216 lower proportions at the highest sound comfort level. 217 Based on the results, we find that increasing the proportion of natural source sounds and 218 more reasonable land use configurations that reduce the proportion of human-made source 219 sounds can be expected to enhance the sound comfort level. However, increasing human 220 activities source sound does not decrease sound comfort. 221

Fig. 9. Percentage of subjective evaluation sound level and sound harmoniousness at different 223 sound comfort levels 224 When the sound comfort level is shifted from very uncomfortable to very comfortable, Fig. 225 9 shows the sound harmoniousness level is also enhanced, whereas the subjective evaluation 226

Table 1 162 The LAeq values of SPM and mobile phones in the same outdoor environment (dBA) 163

Fig. 3. Different mobile phones compared with SPM 161

Fig. 1. Logical architecture for SPL Meter 73

Fig. 4. Daily variation of measured activities 178

Fig. 5. Map of measured sites and percentages in each country 180 3.3. Data quality impacted by gender and age 181 A complete measurement record includes information on LAeq, mLpa, mF, L10, L50, L90, land 182 use, GPS, gender, age, sound sources, and subjective sound evaluation level (level, comfort, 183 and harmoniousness). The first six physical indicators described the sound and are not 184 impacted by the participants’ demographic biases. The subjective soundscape evaluation, 185 sound sources, and class of land use identification, which require knowledge other than 186 gender and age, are uneven in the differences among participants’ demographic biases. Fig. 6 187 shows the record integrity for participants under 12 years old was much lower than that of 188 other age groups. Women show better performance in data integrity (completing the recording) 189 than men. The accuracy of GPS is easily affected by the surroundings, but most distances 190 (81.5%) are less than 50 meters. 191

Fig. 2. PSS online analysis and visualization website 122

Frequently asked questions

Q1. What are the contributions in this paper?

This paper describes 4 Participatory Soundscape Sensing ( PSS ), a worldwide soundscape investigation and 5 evaluation project. The authors describe the calibration method for sound pressure levels ( SPL ) 6 measured by mobile phone, analyze the PSS ’ s data temporal-spatial distribution 7 characteristics, and discuss the impact of the participants ’ age and gender on the data quality. 8 Furthermore, the authors analyze the sound comfort level relationships with each class of land use, 9 sound sources, subjective evaluation, sound level, sound harmoniousness, gender, and age 10 using over a year of shared data. The authors suggest that the 19 crowdsourcing data with participatory sensing will provide a new perspective in soundscape 20 investigation, evaluation, and planning. The results suggest that PSS has distinct advantages in 11 enhancing the amount and coverage of soundscape data.