Quantification of the Psychoacoustic Effect of Noise from Small Unmanned Aerial Vehicles
Abstract
:1. Introduction
- Turbulent inflow noise, which can produce significant ‘quasi-tonal noise’ at harmonics of the blade passing frequency. This noise source requires the flow incident on the propeller disc to be turbulent. Turbulent inflow noise can be predicted using a computational method such as that described in [21] or analytical methods such as that described by [22]. Note that tests conducted in anechoic chambers or confined spaces will produce a recirculating flow, which can produce high levels of turbulent inflow noise [23,24];
- The periodic unsteady blade motion caused by the pulsing motion of the propeller blades can also produce tonal noise [25];
- ‘Self-noise’ caused by the unsteady loading on the propeller blades generated by the turbulent flow in the boundary layers, near wake and tip vortex produced by the rotor blades can produce broadband noise at high frequencies [17];
- Blade vortex interaction and blade wake interaction noise caused by the rotor blades interacting with the tip vortex and wake from the preceding rotor blades can produce broadband noise [16].
- a.
- How annoying is the noise from different types of UAVs operating in different flight modes (i.e., hover vs. flyby, high vs. low altitude)?
- b.
- What effect does UAV altitude have on the annoyance caused by UAV noise?
- c.
- Is there a relationship between the annoyance caused by the noise produced by a hovering UAV and that produced by a UAV in flyby?
- d.
- What metric correlates best with the annoyance caused by the noise from UAVs operating in either hover or in flyby?
- e.
- How does drone noise affect performance during a cognitive distraction test?
2. Quadcopter UAVs and Flight Conditions
3. Noise Recording/Measurement and Analysis
F5 | 5th percentile of the time-varying fluctuation strength in vacil calculated according to the methods described in [31] and ISO 532-1 [47] and calculated using Matlab’s Audio Toolbox; |
LA,eq | A-weighted continuous equivalent sound pressure level; |
LA,f,max | A-weighted maximum sound pressure level (using a fast time weighting); |
LA,f,5 | A-weighted sound pressure level exceeded 5% of the time (using a fast time weighting); |
LPN,m | Mean perceived noise level in dB calculated using the method described in Part 36 of the noise certification requirements of the United States Code of Federal Regulations; |
LPN,5 | 5th percentile perceived noise level in dB calculated using the method described in Part 36 of the noise certification requirements of the United States Code of Federal Regulations; |
LTPN,m | Mean tone-corrected perceived noise level in dB calculated using the method described in Part 36 of the noise certification requirements of the United States Code of Federal Regulations; |
LTPN,5 | 5th percentile tone-corrected perceived noise level in dB calculated using the method described in Part 36 of the noise certification requirements of the United States Code of Federal Regulations; |
Nm | Mean time-varying loudness in sones based on Zwicker’s method and described in ISO 532-1 [47] and calculated using Matlab’s Audio Toolbox; |
N5 | 5th percentile of the time-varying loudness in sones based on Zwicker’s method and described in ISO 532-1 [47] and calculated using Matlab’s Audio Toolbox; |
PA | Zwicker psychoacoustic annoyance (see, for example, [36]) calculated as , where if and if ; and ; |
Rm | Mean time-varying roughness in aspers based on the method described by [31] and ISO 532-1 [47] and calculated using Matlab’s Audio Toolbox; |
R5 | 5th percentile of the time-varying roughness in aspers based on the method described by [31] and ISO 532-1 [47] and calculated using Matlab’s Audio Toolbox; |
S5 | 5th percentile of the time-varying sharpness in acums calculated according to DIN 45692 [48] and ISO 532-1 [47] and calculated using Matlab’s Audio Toolbox. |
LEA | A-weighted sound exposure level calculated over the period in which the noise level was within 10 dB of the maximum A-weighted sound pressure level, or over the entire recording period if the level never dropped below 10 dB of the maximum A-weighted sound pressure level; |
LTPN,max | Maximum tone-corrected perceived noise level in dB calculated using the method described in Part 36 of the noise certification requirements of the United States Code of Federal Regulations. |
4. Psychoacoustic Test Procedure and Data Analysis
4.1. Participant Group
4.2. Sound Reproduction
4.3. Administration of the Questionnaire
Statistical Analysis
5. Results and Discussion
5.1. Noise Level Measurement Results
5.2. Annoyance Rating Analysis
5.3. Correlation between Objective Measurements and Annoyance Ratings
5.4. Effect of LA,f,max on Annoyance Rating
5.5. Cognitive Distraction Test Score Analysis
6. Conclusions and Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
Appendix A
Tello | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
low flyby–low hover | −1.1351 | 0.162 | 2202 | −7.017 | <0.0001 |
Mavic | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
high flyby–low flyby | −0.9189 | 0.162 | 2202 | −5.681 | <0.0001 |
high hover–low hover | −0.8288 | 0.162 | 2202 | −5.124 | <0.0001 |
high flyby–high hover | 0.7297 | 0.162 | 2202 | 4.511 | <0.0001 |
low flyby–low hover | 0.8198 | 0.162 | 2202 | 5.068 | <0.0001 |
Mavic LNR | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
high flyby–low flyby | −0.6757 | 0.162 | 2202 | −4.177 | 0.0002 |
high hover–low hover | −0.6486 | 0.162 | 2202 | −4.01 | 0.0004 |
high flyby–high hover | 0.5045 | 0.162 | 2202 | 3.119 | 0.0099 |
low flyby–low hover | 0.5315 | 0.162 | 2202 | 3.286 | 0.0057 |
Phantom | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
high hover–low hover | −1.6306 | 0.162 | 2202 | −10.08 | <0.0001 |
Matrice | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
high flyby–low flyby | −1.2793 | 0.162 | 2202 | −7.908 | <0.0001 |
high hover–low hover | −2.4955 | 0.162 | 2202 | −15.427 | <0.0001 |
high flyby–high hover | −0.1351 | 0.162 | 2202 | −0.835 | 0.8376 |
low flyby–low hover | −1.3514 | 0.162 | 2202 | −8.354 | <0.0001 |
Matrice PL | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
high flyby–low flyby | −0.0721 | 0.162 | 2202 | −0.446 | 0.9705 |
high hover–low hover | −1.5766 | 0.162 | 2202 | −9.746 | <0.0001 |
high flyby–high hover | 0.9009 | 0.162 | 2202 | 5.569 | <0.0001 |
low flyby–low hover | −0.6036 | 0.162 | 2202 | −3.731 | 0.0011 |
High-Altitude Flyby | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
Mavic–Mavic with LNR | −0.3333 | 0.162 | 2202 | −2.061 | 0.3086 |
Mavic–Matrice | −1.982 | 0.162 | 2202 | −12.252 | <0.0001 |
Mavic–Matrice with PL | −3.9009 | 0.162 | 2202 | −24.115 | <0.0001 |
Mavic with LNR–Matrice | −1.6486 | 0.162 | 2202 | −10.192 | <0.0001 |
Mavic with LNR–Matrice with PL | −3.5676 | 0.162 | 2202 | −22.054 | <0.0001 |
Matrice–Matrice with PL | −1.9189 | 0.162 | 2202 | −11.862 | <0.0001 |
Low-Altitude Flyby | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
Mavic–Mavic with LNR | −0.0901 | 0.162 | 2202 | −0.557 | 0.9937 |
Mavic–Matrice | −2.3423 | 0.162 | 2202 | −14.48 | <0.0001 |
Mavic–Matrice with PL | −3.0541 | 0.162 | 2202 | −18.88 | <0.0001 |
Mavic–Tello | 2.2342 | 0.162 | 2202 | 13.812 | <0.0001 |
Mavic with LNR–Matrice | −2.2523 | 0.162 | 2202 | −13.923 | <0.0001 |
Mavic with LNR–Matrice with PL | −2.964 | 0.162 | 2202 | −18.323 | <0.0001 |
Mavic with LNR–Tello | 2.3243 | 0.162 | 2202 | 14.369 | <0.0001 |
Matrice–Matrice with PL | −0.7117 | 0.162 | 2202 | −4.4 | 0.0002 |
Matrice–Tello | 4.5766 | 0.162 | 2202 | 28.292 | <0.0001 |
Matrice with PL–Tello | 5.2883 | 0.162 | 2202 | 32.691 | <0.0001 |
High-Altitude Hover | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
Mavic–Mavic with LNR | −0.5586 | 0.162 | 2202 | −3.453 | 0.0075 |
Mavic–Phantom | −1.5405 | 0.162 | 2202 | −9.523 | <0.0001 |
Mavic–Matrice | −2.8468 | 0.162 | 2202 | −17.599 | <0.0001 |
Mavic–Matrice with PL | −3.7297 | 0.162 | 2202 | −23.057 | <0.0001 |
Mavic with LNR–Phantom | −0.982 | 0.162 | 2202 | −6.07 | <0.0001 |
Mavic with LNR–Matrice | −2.2883 | 0.162 | 2202 | −14.146 | <0.0001 |
Mavic with LNR–Matrice with PL | −3.1712 | 0.162 | 2202 | −19.604 | <0.0001 |
Phantom–Matrice | −1.3063 | 0.162 | 2202 | −8.075 | <0.0001 |
Phantom–Matrice with PL | −2.1892 | 0.162 | 2202 | −13.533 | <0.0001 |
Matrice–Matrice with PL | −0.8829 | 0.162 | 2202 | −5.458 | <0.0001 |
Low-Altitude Hover | |||||
Contrast | Estimate | SE | df | t-ratio | p-value |
Mavic–Mavic with LNR | −0.3784 | 0.162 | 2202 | −2.339 | 0.1789 |
Mavic–Phantom | −2.3423 | 0.162 | 2202 | −14.48 | <0.0001 |
Mavic–Matrice | −4.5135 | 0.162 | 2202 | −27.902 | <0.0001 |
Mavic–Matrice with PL | −4.4775 | 0.162 | 2202 | −27.679 | <0.0001 |
Mavic–Tello | 0.2793 | 0.162 | 2202 | 1.726 | 0.5143 |
Mavic with LNR–Phantom | −1.964 | 0.162 | 2202 | −12.141 | <0.0001 |
Mavic with LNR–Matrice | −4.1351 | 0.162 | 2202 | −25.563 | <0.0001 |
Mavic with LNR–Matrice with PL | −4.0991 | 0.162 | 2202 | −25.34 | <0.0001 |
Mavic with LNR–Tello | 0.6577 | 0.162 | 2202 | 4.066 | 0.0007 |
Phantom–Matrice | −2.1712 | 0.162 | 2202 | −13.422 | <0.0001 |
Phantom–Matrice with PL | −2.1351 | 0.162 | 2202 | −13.199 | <0.0001 |
Phantom–Tello | 2.6216 | 0.162 | 2202 | 16.206 | <0.0001 |
Matrice–Matrice with PL | 0.036 | 0.162 | 2202 | 0.223 | 0.9999 |
Matrice–Tello | 4.7928 | 0.162 | 2202 | 29.628 | <0.0001 |
Matrice with PL–Tello | 4.7568 | 0.162 | 2202 | 29.405 | <0.0001 |
Mavic SE = 0.11, df = 7499 | Mavic with LNR SE = 0.11, df = 7499−7500 | |||||
Contrast | Estimate | t-ratio | p-value | Estimate | t-ratio | p-value |
High flyby–low flyby | −0.259 | −2.31 | 0.0959 | 0.287 | 2.581 | 0.0485 |
Low flyby–low hover | 0.037 | 0.329 | 0.9877 | −0.172 | −1.542 | 0.4124 |
High flyby–high hover | −0.289 | −2.59 | 0.0473 | 0.078 | 0.702 | 0.8964 |
High hover–low hover | 0.067 | 0.603 | 0.9312 | 0.038 | 0.34 | 0.9865 |
Matrice SE = 0.11, df = 7499–7500 | Matrice with PL SE = 0.11, df = 7499 | |||||
Contrast | Estimate | t-ratio | p-value | Estimate | t-ratio | p-value |
High flyby–low flyby | −0.248 | −2.218 | 0.1185 | −0.081 | −0.722 | 0.8885 |
Low flyby–low hover | 0.397 | 3.563 | 0.0021 | −0.077 | −0.692 | 0.9001 |
High flyby–high hover | −0.250 | −2.244 | 0.1116 | −0.336 | −3.009 | 0.014 |
High hover–low hover | 0.399 | 3.598 | 0.0018 | 0.178 | 1.599 | 0.3792 |
Phantom SE = 0.11, df = 7499 | Tello SE = 0.11, df = 7499 | |||||
Contrast | Estimate | t-ratio | p-value | Estimate | t-ratio | p-value |
Low flyby–low hover | n/a | n/a | n/a | 0.052 | 0.463 | 0.9671 |
High hover–low hover | −0.023 | −0.204 | 0.997 | n/a | n/a | n/a |
Low, Hover | Low, Flyby | |||||
Contrast | Estimate | t-ratio | p-value | Estimate | t-ratio | p-value |
Mavic–Mavic with LNR | −0.03521 | −0.317 | 0.9996 | 0.17312 | 1.55 | 0.6317 |
Mavic–Phantom | −0.00755 | −0.068 | 1 | n/a | n/a | n/a |
Mavic–Matrice | 0.15791 | 1.422 | 0.7138 | −0.20188 | −1.808 | 0.4608 |
Mavic–Matrice with PL | −0.11671 | −1.05 | 0.9008 | −0.0027 | −0.024 | 1 |
Mavic–Tello | 0.02299 | 0.207 | 0.9999 | 0.00809 | 0.072 | 1 |
Mavic with LNR–Phantom | 0.02766 | 0.25 | 0.9999 | n/a | n/a | n/a |
Mavic with LNR–Matrice | 0.19312 | 1.74 | 0.5052 | −0.375 | −3.37 | 0.0098 |
Mavic with LNR–Matrice with PL | −0.0815 | −0.734 | 0.9778 | −0.17581 | −1.574 | 0.6157 |
Mavic with LNR–Tello | 0.0582 | 0.524 | 0.9952 | −0.16503 | −1.478 | 0.6787 |
Phantom–Matrice | 0.16546 | 1.494 | 0.6684 | n/a | n/a | n/a |
Phantom–Matrice with PL | −0.10916 | −0.985 | 0.9229 | n/a | n/a | n/a |
Phantom–Tello | 0.03054 | 0.276 | 0.9998 | n/a | n/a | n/a |
Matrice–Matrice with PL | −0.27462 | −2.473 | 0.1324 | 0.19919 | 1.783 | 0.4766 |
Matrice–Tello | −0.13492 | −1.216 | 0.8294 | 0.20997 | 1.88 | 0.4146 |
MatricePL–Tello | 0.1397 | 1.258 | 0.8079 | 0.01078 | 0.096 | 1 |
High, Hover | High, Flyby | |||||
Contrast | Estimate | t-ratio | p-value | Estimate | t-ratio | p-value |
Mavic–Mavic with LNR | −0.0059 | −0.053 | 1 | −0.37287 | −3.339 | 0.0109 |
Mavic–Phantom | 0.08201 | 0.739 | 0.9771 | n/a | n/a | n/a |
Mavic–Matrice | −0.174 | −1.57 | 0.6187 | −0.21294 | −1.901 | 0.4017 |
Mavic–Matrice with PL | −0.22747 | −2.048 | 0.3152 | −0.18059 | −1.612 | 0.5906 |
Mavic with LNR–Phantom | 0.08791 | 0.793 | 0.9688 | n/a | n/a | n/a |
Mavic with LNR–Matrice | −0.16811 | −1.518 | 0.6523 | 0.15994 | 1.432 | 0.7075 |
Mavic with LNR–Matrice with PL | −0.22157 | −1.998 | 0.3437 | 0.19228 | 1.722 | 0.5174 |
Phantom–Matrice | −0.25602 | −2.31 | 0.1902 | n/a | n/a | n/a |
Phantom–Matrice with PL | −0.30948 | −2.786 | 0.0597 | n/a | n/a | n/a |
Matrice–Matrice with PL | −0.05347 | −0.482 | 0.9968 | 0.03234 | 0.289 | 0.9997 |
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No. | UAV | Flight Condition | Height of UAV | No. | UAV | Flight Condition | Height of UAV |
---|---|---|---|---|---|---|---|
1 | Tello | SLF | 10 m | 11 | Phantom | Hover | 10 m |
2 | Tello | Hover | 10 m | 12 | Phantom | Hover | 27 m |
3 | Mavic | SLF | 10 m | 13 | Matrice | SLF | 10 m |
4 | Mavic | SLF | 30 m | 14 | Matrice | SLF | 30 m |
5 | Mavic | Hover | 10 m | 15 | Matrice | Hover | 10 m |
6 | Mavic | Hover | 30 m | 16 | Matrice | Hover | 30 m |
7 | Mavic with LNR | SLF | 10 m | 17 | Matrice with PL | SLF | 10 m |
8 | Mavic with LNR | SLF | 30 m | 18 | Matrice with PL | SLF | 30 m |
9 | Mavic with LNR | Hover | 10 m | 19 | Matrice with PL | Hover | 10 m |
10 | Mavic with LNR | Hover | 30 m | 20 | Matrice with PL | Hover | 30 m |
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Hui, C.T.J.; Kingan, M.J.; Hioka, Y.; Schmid, G.; Dodd, G.; Dirks, K.N.; Edlin, S.; Mascarenhas, S.; Shim, Y.-M. Quantification of the Psychoacoustic Effect of Noise from Small Unmanned Aerial Vehicles. Int. J. Environ. Res. Public Health 2021, 18, 8893. https://doi.org/10.3390/ijerph18178893
Hui CTJ, Kingan MJ, Hioka Y, Schmid G, Dodd G, Dirks KN, Edlin S, Mascarenhas S, Shim Y-M. Quantification of the Psychoacoustic Effect of Noise from Small Unmanned Aerial Vehicles. International Journal of Environmental Research and Public Health. 2021; 18(17):8893. https://doi.org/10.3390/ijerph18178893
Chicago/Turabian StyleHui, C. T. Justine, Michael J. Kingan, Yusuke Hioka, Gian Schmid, George Dodd, Kim N. Dirks, Shaun Edlin, Sean Mascarenhas, and Young-Min Shim. 2021. "Quantification of the Psychoacoustic Effect of Noise from Small Unmanned Aerial Vehicles" International Journal of Environmental Research and Public Health 18, no. 17: 8893. https://doi.org/10.3390/ijerph18178893