Sound Masking Strategies for Interference with Mosquito Hearing

TL;DR

The paper tackles how to disrupt active hearing in mosquitoes and a generic auditory detector using acoustic masking, formalizing the problem with two Hopf-oscillator models and quantifying detectability via transfer entropy $T_{J\rightarrow I}$. By constraining mask power and exploring filtered-noise, two-tone, AM, and FM masks, it finds that masks concentrating energy into a single frequency and employing rapid frequency modulation most effectively reduce information transfer. Across both models, FM masks outperform other forms, suggesting a barrage-jamming-like approach could impede mosquito communication in practice, while AM and multi-tone masks offer limited improvement. The work highlights practical prospects for population control and outlines future directions, including combined AM/FM schemes and machine-learning-driven mask optimization for real-world conditions.

Abstract

The use of auditory masking has long been of interest in psychoacoustics and for engineering purposes, in order to cover sounds that are disruptive to humans or to species whose habitats overlap with ours. In most cases, we seek to minimize the disturbances to the communication of wildlife. However, in the case of pathogen-carrying insects, we may want to maximize these disturbances as a way to control populations. In the current work, we explore candidate masking strategies for a generic model of active auditory systems and a model of the mosquito auditory system. For both models, we find that masks with all acoustic power focused into just one or a few frequencies perform best. We propose that masks based on rapid frequency modulation are most effective for maximal disruption of information transfer and minimizing intelligibility. We hope that these results will serve to guide the avoidance or selection of possible acoustic signals for, respectively, maximizing or minimizing communication.

Figures (9)

• Figure 1: Demonstration of the effects of a filtered-noise mask. The target signal and masking signal are shown in black and pink, respectively. The responses of model 1 (blue) and model 2 (orange) are shown in both the time and frequency domains. The top two rows show the responses in the absence of the masking signal, while the bottom two rows show the responses in the presence of the masking signal.
• Figure 2: Filtered-noise mask. The effects of the masking parameters, $\omega$ and $\sigma_{\omega}$, on the transfer entropy from $F_f(t)$ to $z(t)$ (model 1: M1), and from $F_f(t)$ to $z_2(t)$ (model 2: M2).
• Figure 3: Two-tone mask. The effects of the masking parameters, $\Omega_2$ and $A_2/A_1$, on the transfer entropy from $F_f(t)$ to $z(t)$ (model 1: M1), and from $F_f(t)$ to $z_2(t)$ (model 2: M2).
• Figure 4: Amplitude-modulation (AM) mask. The effects of the masking parameters, $\omega_{mod}$ and $A_{mod}$, on the transfer entropy from $F_f(t)$ to $z(t)$ (model 1: M1), and from $F_f(t)$ to $z_2(t)$ (model 2: M2).
• Figure 5: Power-law frequency-modulation (FM) mask. The effects of the masking parameters, $\omega_{mod}$ and $A_{mod}$, on the transfer entropy from $F_f(t)$ to $z(t)$ (model 1: M1), and from $F_f(t)$ to $z_2(t)$ (model 2: M2).