What makes drones sound annoying? The answer may lie in noise fluctuations

Ze Feng (Ted) Gan – tedgan@psu.edu

Department of Aerospace Engineering, The Pennsylvania State University, University Park, PA, 16802, United States

Popular version of 2aNSa3 – Multirotor broadband noise modulation
Presented at the 186th ASA Meeting
Read the abstract at https://eppro02.ativ.me/web/index.php?page=IntHtml&project=ASASPRING24&id=3673871

–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–

Picture yourself strolling through a quiet park. Suddenly, you are interrupted by the “buzz” of a multirotor drone. You ask yourself: why does this sound so annoying? Research demonstrates that a significant source is the time variation of broadband noise levels over a rotor revolution. These noise fluctuations have been found to be important for how we perceive sound. This research has found that these sound variations are significantly affected by the blade angle offsets (azimuthal phasing) between different rotors. This demonstrates the potential for synchronizing the rotors to reduce noise: a concept that has been studied extensively for tonal noise, but not broadband noise.

Sound consists of air pressure fluctuations. One major source of sound generated by rotors consists of the random air pressure fluctuations of turbulence, which encompass a wide range of frequencies. Accordingly, this sound is called broadband noise. A common example and model of broadband noise is white noise, shown in Figure 1, where the random nature characteristic of broadband noise is evident. Despite this randomness, we hear the noise of Figure 1 as having a nearly constant sound level.

Figure 1: White noise with a nearly constant sound level.

A better model of rotor noise is white noise with amplitude modulation (AM). Amplitude modulation is caused by the blades’ rotation: sound levels are louder when the blade moves towards the listener, and quieter when the blade moves away. This is called Doppler amplification, and is analogous to the Doppler effect that shifts sound frequency when a sound source travels towards or away from you. AM white noise is shown in Figure 2: the sound is still random, but has a sinusoidal “envelope” with a modulation frequency corresponding to the blade passage frequency. AM causes time-varying sound levels, as shown in Figure 3. This time variation is characterized by the modulation depth, the peak-to-trough amplitude in decibels (dB), as shown in Figure 3. A greater value for modulation depth typically corresponds to the noise sounding more annoying.

Figure 2: White noise with amplitude modulation (AM).
Figure 3: Time-varying sound levels of AM white noise.

Broadband noise modulation is known to be important for wind turbines, whose “swishing” is found to be annoying even at low sound levels. This contrasts with white noise, which is typically considered soothing when it has a constant sound level (i.e., no AM). This exemplifies the importance of considering time variation of sound levels for capturing human perception of sound. More recently, the importance of broadband noise modulation has been demonstrated for helicopters, as this chopping noise is what makes a helicopter sound like a realistic helicopter, even if it has low sound levels.

Researchers have not extensively studied broadband noise modulation for aircraft with many rotors. Computational studies in the literature predict that summing the broadband noise modulation of many rotors causes “destructive interference”, resulting in nearly no modulation. However, flight test measurements of a six-rotor drone showed that broadband noise modulation was significant. To investigate this discrepancy, changes in modulation depth were studied as the blade angle offset between rotors was varied. This offset is typically not considered in noise predictions and experiments. The results are shown in Figure 4. For each data point in Figure 4, the rotor rotation speeds are synchronized, but the value for the constant blade angle offset between rotors is different. The results of Figure 4 demonstrate the potential for synchronizing rotors to reduce broadband noise modulation. This synchronization controls the blade angle offset between rotors to be as constant as possible, and has been extensively studied for controlling tones (sounds at a single frequency), but not broadband noise modulation.

Figure 4: Modulation depth as a function of blade angle offset between two synchronized rotors.

If the rotors are not synchronized, which is typically the case, the flight controller continuously varies the rotors’ rotation speeds to stabilize or maneuver the drone. This causes the blade angle offsets between rotors to with vary with time, which in turn causes the summed noise to vary between different points in Figure 4. Measurements showed that all rotor blade angle offsets are equally likely (i.e., azimuthal phasing follows a uniform probability distribution). Therefore, multirotor broadband noise modulation can be characterized and predicted by constructing a plot like Figure 4, by adding copies of the broadband noise modulation of a single rotor.