Wearable Microphone Jamming
Yuxin Chen∗

Huiying Li∗ Shan-Yuan Teng∗ Steven Nagels Zhijing Li
Pedro Lopes Ben Y. Zhao Haitao Zheng
University of Chicago{yxchen, huiyingli, tengshanyuan, stevennagels, zhijing,pedrolopes, ravenben, htzheng}@cs.uchicago.edu∗ equal contribution

ABSTRACTWe engineered a wearable microphone jammer that is capableof disabling microphones in its user’s surroundings, including
hidden microphones. Our device is based on a recent exploitthat leverages the fact that when exposed to ultrasonic noise,commodity microphones will leak the noise into the audible
range.
Unfortunately, ultrasonic jammers are built from multiple
transducers and therefore exhibit blind spots, i.e., locations inwhich transducers destructively interfere and where a microphone cannot be jammed. To solve this, our device exploits asynergy between ultrasonic jamming and the naturally occurring movements that users induce on their wearable devices(e.g., bracelets) as they gesture or walk. We demonstrate thatthese movements can blur jamming blind spots and increase
jamming coverage. Moreover, current jammers are also directional, requiring users to point the jammer to a microphone;instead, our wearable bracelet is built in a ring-layout that allows it to jam in multiple directions. This is beneficial in thatit allows our jammer to protect against microphones hidden
out of sight.
We evaluated our jammer in a series of experiments and found
that: (1) it jams in all directions, e.g., our device jams over
87% of the words uttered around it in any direction, while
existing devices jam only 30% when not pointed directly at
the microphone; (2) it exhibits significantly less blind spots;
and, (3) our device induced a feeling of privacy to participants of our user study. We believe our wearable provides
stronger privacy in a world in which most devices are constantly eavesdropping on our conversations.
CCS Concepts•Human-centered computing → Interaction devices;
Sound-based input / output; Ubiquitous computing;Author Keywords

Wearable; microphone; jamming; privacy; ultrasoundPermission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than theauthor(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
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and/or a fee. Request permissions from permissions@acm.org.
CHI ’20, April 25–30, 2020, Honolulu, HI, USA.
© 2020 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-6708-0/20/04 ...$15.00.
http://dx.doi.org/10.1145/3313831.3376304

a

voice
assistant(jammed)smartphone(jammed)smartwatch(jammed)wearable jammer

b original voice
he was in deep

converse

with

the

clerk

and

enter

the hall

?

an

entry

your

c recorded voice (jammed)
he was on

?

?

?

?

Figure 1. (a) We engineered a wearable ultrasound jammer that can
prevent surrounding microphones from eavesdropping on a conversation. (b) This is the actual speech that the conversation partner hears,
since our jammer does not disrupt human hearing. However, (c) is thetranscript of what a state-of-the-art speech recognizer makes out of thejammed conversation.

INTRODUCTION

Despite the initial excitement around voice-based smart devices, consumers are becoming increasingly nervous with thefact that these interactive devices are, by default, always listening, recording, and possibly saving sensitive personal information [32, 55, 38, 26]. Take digital voice assistants,
which are featured in most smartphones, smartwatches, andsmart speakers, as an example. From the outside, these interactive assistants appear to only respond to designated wakeup words (e.g., “Alexa” and “Hey Google”). However, theirimplementation requires them to listen continuously to detect these wake-up words. It has been shown that these devices can monitor and record sounds and conversations inreal time, either maliciously [56], by misconfiguration [26],or after compromise by attackers [51]. Leaked audio data canbe processed to extract confidential information [56, 16, 15],
track user activity [8], count human speakers [57], or even extract handwriting content [58]. These negative implications
on users’ security and privacy are significant and unacceptable. To make matters worse, many other acoustic attacks

(e.g., turning speakers into microphones [25], inferring thecontent of a printed page by recording its printing [9], inferring a 3D object’s geometry by recording its printing [22], inferring typed text by listening to key presses [6, 61]) as wellas many forms of espionage (e.g., industrial espionage [17,28]) rely on eavesdropping via hidden microphones.
Therefore, it is critical to build tools that protect users against
the potential compromise or misuse of microphones in theage of voice-based smart devices. Recently, researchers haveshown that ultrasonic transducers can prevent commodity microphones from recording human speech [46]. While theseultrasonic signals are imperceptible to human ears, they leakinto the audible spectrum after being captured by the microphones, producing a jamming signal inside the microphone
circuit that jams (disrupts) voice recordings. The leakage iscaused by an inherent, nonlinear property of microphone’s
hardware. Not only have researchers built prototypes using
ultrasonic speakers [46], but also these jammers are currently
commercially available to the public. However, all these devices exhibit two key limitations: (1) They are heavily directional, thus requiring users to point the jammer precisely at
the location where the microphones are. This is not only impractical, as it interferes with the users’ primary task, but isalso often impossible when microphones are hidden. (2) They
rely on multiple transducers that enlarge their jamming coverage but introduce blind spots—locations were the signals
from two or more transducers cancel each other out. Suchblind spots occur especially in close proximity to the jammer;in fact, 17% of all locations within 1.2m of a typical multitransducer jammer are blind spots. If a microphone is placedin any of these locations it will not be jammed, rendering thewhole jammer obsolete.
To tackle these shortcomings, we engineered a wearable jammer that is worn as a bracelet, which is depicted in Figure 1.
By turning an ultrasonic jammer into a bracelet, our device
leverages natural hand gestures that occur while speaking,gesturing or moving around to blur out the aforementioned
blind spots. Furthermore, by arranging the transducers in aring layout, our wearable jams in multiple directions and protects the privacy of its user’s voice, anywhere and anytime,
without requiring its user to manually point the jammer to theeavesdropping microphones.
We confirmed that an ultrasonic microphone jammer is superior to state-of-the-art and commercial stationary jammers byconducting a series of technical evaluations and a user study.
These demonstrated that: (1) our wearable jammer outperformed static jammers in jamming coverage; (2) its jamming
is effective even if the microphones are hidden and covered byvarious materials, such as cloths or paper sheets; and, (3) in alife-like situation our study participants felt that our wearable
protected the privacy of their voice.
BACKGROUND AND RELATED WORK

Our work builds on top of ultrasonic emitters and wearables.
Also, we discuss the implications of data leaks in interactive devices, especially those with microphones and cameras.
Lastly, we introduce the underlying ultrasonic jamming principle that our device is based on.

Privacy issues with interactive devices

As various interactive devices being deployed into daily life,
privacy issues arise since these devices often rely on constantcapturing of multimedia, such as photos, videos, or sound, inorder to provide services that assist users’ activities [11, 42].
Researchers have proposed privacy-aware methods to collect user’s data by, for instance, designing improved notifications [36] or exploring configurations that are privacyconscientious [2]. These approaches are, however, developercentric and thus require that the user trusts the interactive system. The result is that these approaches are beneficial but nota fail-proof solution, as devices are still exposed to attackers.
Lastly, these solutions do not seek to empower the users toactively protect their privacy.
For example, as the privacy implications of cameras grew inimportance, webcams started to use lights that indicate theirrecording state [43]. However, these indicators can be disabled by attackers [13], which led to many users opting forphysically covering up the webcams [31].
More recently, digital assistant devices, such as Amazon
Echo, have become very popular due to their interactive (conversational) ability. These interactive devices are built with amicrophone and a speaker. To interact with the user whenneeded, these voice assistants are designed to respond to designated wake-up words (e.g., “Alexa” and “Hey Google”).
However, continuously listening is required to detect thesewake-up words—this has resulted in many worldwide security breaches, where it was found that these devices leakedor saved sensitive personal information from their users [32,55, 38, 26]. It was shown that these devices can monitor andrecord all voices, sounds and conversations in real time, either
maliciously [56], by misconfiguration [26], or after compromise by attackers [51]. The leaked audio can be further processed to extract confidential information [56, 16, 15], trackuser activity [8], count human speakers [57], and so forth.
One sane option is certainly to turning these devices off one
by one. Unfortunately, that still leaves eavesdropping devices that the user cannot control or that the user is simply
not aware of. Instead of turning off all the devices manually,
microphone jammers aim at empowering users with a tool to
disrupt (jam) voice recordings whenever and wherever theywant, providing a physical layer of privacy on demand.
Principles of ultrasonic microphone jamming

Recent work has demonstrated the feasibility of using ultrasonic transducers to disable nearby microphones [46]. The
advantage of jamming by means of ultrasound is that it is“silent” to users, as ultrasound is inaudible to humans. We illustrate this type of jamming in Figure 2. Ultrasonic jammingis possible because these higher-frequency signals, after being captured by the microphone’s non-linear diaphragm andpower-amplifier, will create a lower-frequency "shadow" that
happens to be in the microphone’s filtering range—the audible range [46]. This technique works against billions of commodity microphones (found in phones, laptops, voice assistants, etc.), without any microphone modification. The fundamental exploit is due to the fact that acoustic amplifiers are
only linear around the audible frequency range, while outsideamplitude (dB)inaudible to humans-20leakage due tonon-linear
ampliﬁcation-30
covering human voice

-40
0

5k

10k

15k
20k
frequency (Hz)

25k

30k

Figure 2. Working principle behind ultrasonic jamming (similar to [46]but here at the example of a 25kHz signal). Here, we depict how an ultrasonic jamming signal (shown in blue), which is inaudible to humans,
still leaks into the recorded speech due to non-linear amplification of the
microphone’s circuit. The result is that the leaked signal covers up precisely the spectrum in which a user’s voice is recorded (shown in black).

A WEARABLE JAMMER BRACELETWe engineered a microphone jammer in a wearable form factor, which effectively jams in more directions around the user
than existing approaches. To assist the reader in replicating
our device, we describe the implementation details and thekey design elements that enabled our wearable jammer to outperform existing jammers.
We designed our wearable jammer as a bracelet so that it canbe easily activated [59, 54, 19, 41, 3] whenever the user decides to engage in a private conversation. Having the device
at users’ reach at all times provides them with “always available input” [49], ensuring the user is the one in control.
Implementationof the range (e.g., ultrasound), the amplifier’s response exhibits non-linearities [46, 1]. This leakage from ultrasound to
audible range adds so much audible noise on the microphone
circuitry that it effectively renders voice recordings unusable.
Leveraging microphone non-linearity

This non-linearity in microphone circuitry was originally discovered by musicians and leveraged for sound synthesis [29].
Only more recently, have researchers leveraged these nonlinearities as a potential tool for setting up hidden communication channels, disabling microphones, or as an adversarial avenue for injecting hidden voice commands. A series ofprojects leveraged this property to attack digital voice assistants [60, 51, 47]. Here, an adversary can play (arbitrary)voice commands modulated in the ultrasonic range to digital assistants and force these devices to decode them as normal voice commands. Since the original ultrasonic commandis inaudible, the attacker can successfully issue commands
without being detected (i.e., heard) by nearby users.
Similarly, backdoor [46] leverages non-linearity to build an
inaudible communication channel among devices and to jam
microphones. The backdoor jams based on either amplitude
modulation (AM) or frequency modulation (FM). backdoor
was tested in a limited set of experiments with the jammer
pointing to a single microphone. Nowadays, there are commercial ultrasonic jammers, such as the i4. Unfortunately, although all of them are large, bulky (0.38kg–5kg), and pricey($799–$6900) [21, 20, 39, 27]. These jammers have also alimited angular coverage and require the users to point directly at the microphone. This is disadvantageous in thatthese jammers require user’s attention to operate and cannot
be used against hidden microphones. Inspired by these devices, we propose a novel approach that, instead, leverages
the advantages of a wearable design to enhance jamming effectiveness.
Wearable devices based on ultrasoundResearchers have used signals in ultrasonic bands [7, 35] andnear-ultrasonic bands (e.g., 18.8kHz) [14, 24] to enable interaction with/among devices. As an example, Gupta et al.,utilize Doppler shifts in emitted ultrasound to enable a laptop to perform gesture tracking [24]. A variety of smartphone
applications use ultrasonic signals as beacons to perform device localization and tracking [5, 53, 23], again based on the
aforementioned leakage to the audible band.

To help readers replicate our design, we now provide the necessary technical details. Furthermore, to accelerate replication, we provide all the source code, firmware, and schematics of our implementation 1 .
Our prototype, which is depicted in Figure 3, is a selfcontained wearable device comprised of the following components: a 3D-printed 9cm ring (outer diameter) with a slitthat acts as a hinge, allowing the wearer to open up thebracelet and fit it around their arm; 23 ultrasound transducers (NU25C16T-1, 25kHz), featuring 12 on the lower ringand 11 on the top ring (one transducer was removed to makespace for the aforementioned hinge); a low-power signal generator (AD9833, up to 12.5MHz with 0.004Hz programmablesteps); an ATMEGA32U4 microprocessor; an LED status indicator; a tactile switch (not shown); a LiPo battery (3.7V,
500mAh); a 3W audio amplifier (PAM8403), and, a 3.7V to
5V step-up regulator. Our microprocessor controls the signal
generator via Serial Peripheral Interface.
lithium battery
microcontroller
signal generator

power regulator
3W ampliﬁer

ultrasonic transducer

Figure 3. Our prototype is a self-contained wearable comprised of ultrasonic transducers, a signal generator, a microcontroller, a battery, avoltage regulator and a 3W amplifier.

Signal generation

We generate our ultrasound jamming signal via the AD9833
sine wave generator. This integrated circuit (IC) produces apure sine wave at a desired frequency up to 12.5MHz. To
select our sine wave’s frequency, we control the AD9833
using our microcontroller via SPI2 . In order to jam effectively, we produce not only one frequency but a range of
frequencies. According to the principles of ultrasonic jamming, each of these will produce a shadow at an audible frequency; therefore, using multiple frequencies enhances jamming. We implement our signal by sweeping the frequency
1 http://sandlab.cs.uchicago.edu/jammer
2 https://github.com/Billwilliams1952/AD9833-Library-Arduino

of the sine wave randomly between 24kHz to 26kHz (i.e.,
25kHz±1kHz) in steps of 1Hz. Our sine wave frequencychanges every 0.45 ms. In our earlier designs, we employeda 92kHz wave player IC that played back the white noise(25kHz±1kHz). However, we found that via empirical testing that our randomly-sweeping sine wave yielded the same
jamming power with significantly less power consumption
than the overly complex wave-player IC. Lastly, we amplify
our signal using a 3W amplifier (PAM8403). Note that we set
the amplifier to operate below maximum amplification. This
reduces our power consumption and preserves signal quality(low distortion). When measured directly at any of the transducers, the loudness of our device is around 92.3dBA.
Wearable characteristics: power and weight

We measured the energy consumption of our prototype
bracelet. It consumes approximately 0.47W (3.7V × 127mA)
when jamming, which is ten times less energy than that usedby the commercially available i4 jammer. Thus it can continuously jam for around four hours on our 500mA battery.
Furthermore, our device and battery weigh 135 grams.
Key design elements

Our device was designed with three key elements that allow
it to outperform state-of-the-art microphone jammers.
1. Multi-directional jamming using a ring layout. Existing
microphone jammers, such as backdoor and i4, embed theirultrasonic transducers in a flat (1D or 2D) layout. As a result,these jammers are effective only when the user points themto the target microphone. This is disadvantageous as: (1) it
requires the user to steer the device, making the jamming action a primary task; and, (2) it is practically impossible touse against hidden microphones. Instead, our prototype features all its ultrasonic transducers in a ring layout, effectively
enabling jamming in multiple directions on a plane. We will
later demonstrate that our design is superior by means of both
simulations and experimental evaluations.
2. Reducing blind spots by leveraging naturally-occurring
movements. A significant benefit of proposing a microphonejammer as a wearable device is that we can mitigate the traditional blind spot problem, which affects all transducer arrays, by leveraging naturally-occurring movements. While auser is wearing our jammer, the device is, most of the times,
being moved as the user walks, gestures, points, types, etc.
It is precisely these movements that we leverage to reduce
blind spots, because as the device moves in space the signal
emission map moves accordingly and creates new areas ofincreased signal strength that blur out the blind spot areas.
3. Collocation with the user’s voice. The last design element that makes a wearable design superior is its ubiquitousness. A wearable jammer is collocated with the user that it
protects, whereas stationary jammers need to be installed or
moved around in every space the user inhabits. Furthermore,
the short distance between the jammer and the speaker’s
mouth prevents the use of beamforming microphone arrays toseparate the signals of the human speaker and the jammer [4],making the wearable jammer a stronger defense.

OVERVIEW OF EXPERIMENTS AND STUDY

In order to validate that wearable microphone jammers outperform existing approaches, we conducted simulations andthree experimental evaluations. Lastly, to understand how
participants perceive the effectiveness of our wearable jammer, we conducted a user study. To aid the reader in understanding the different validations we performed, we present
an overview of our simulations, experiments and study:
1. Simulating jammer layouts. Prior to designing our jammer, we confirmed by means of simulation that a wearable
bracelet with a ring-layout reduces blind spots when compared to stationary jammers with planar-layouts. To do so,
we simulated the power of an ultrasonic signal in space after
it leaves the transducer. With our simulations we found that
(1) jammers with transducers in a planar layout jam mostly
in one direction; (2) on the contrary, positioning the transducers in a ring layout increases jamming in multiple directions; and, (3) adding small (simulated) movement, which occurs naturally in a wearable device, results in a blind spot
reduction, similar to what can be achieved using more complex control techniques with multi-frequency signals. Thisfinding is critical because: (1) it allows us to keep the device’s design and circuit simple (i.e., using a single signal
source), which reduces power consumption, making it compatible with a wearable form factor; (2) our approach does
not sacrifice jamming quality when compared to a more complex and hardware heavy approach (i.e., using multiple signal
sources). These findings informed how we created our prototype, which we used in all subsequent experiments and user
study.
2. Experiment#1: angular power distribution. We measured the angular power distribution of our wearable jammer
and both existing devices (a planar jammer with 9 transducers
and the commercially available i4). We found that our device
provides a wide-spread angular coverage (M = −3.3dBA,
SD = 1.6dBA), while the existing jammers are highly directional (planar jammer: M = −19.2dBA, SD = 8.5dBA; i4:
M = −17.0dBA, SD = 6.8dBA).
3. Experiment#2: jamming speech recognizers. We measured how effectively our wearable device jams speech recognizers at different angles, when compared to a planar jammer and i4. We found that our wearable device jams more
effectively in multiple directions with an increased word error rate (WER) when compared to the other jammers (ourwearable: M = 96.59% WER, SD = 3.97%; planar jammer:
M = 38.89% WER, SD = 21.72%; i4: M = 57.55% WER,
SD = 35.04%).
4. Experiment#3: jamming microphones covered by everyday materials. We evaluated how our wearable jams
microphones that are covered with everyday materials (i.e.,
hidden microphones inside boxes, behind clothes, etc.); thisstems from a unique feature of our device as it does not require pointing to the target microphone. We found that ourdevice jams microphones hidden under a variety of objects,
such as ordinary cloths, foam-based microphone windshields
or paper sheets, with a word error rate above 97%.

5. User study. Lastly, we evaluated whether wearing our
jamming bracelet impacted participants’ perception of privacy. In our study, we asked groups of participants to engage
in life-like conversations while they wore the bracelet one ata time. We found that participants felt our wearable protects
their privacy (M=5.4 out of 7, SD=1.1).
SIMULATING JAMMER LAYOUTS

Prior to designing our jamming bracelet, we explored, via
simulations, whether a wearable would be beneficial. We
were interested in answering three questions: (1) how directional are jammers based on planar transducer layouts (e.g.,i4)?; (2) how do blind spots affect a jammer with its transducers in a ring layout?; and, (3) how do the blind spots behave with respect to small movements of the jammer? All
the following simulations were conducted using Matlab. For
researchers interesting in replicating our simulations we provide their source code3 .
Simulation parameters

Generally speaking, our simulation computed the propagation of ultrasound from our sources to all points around thedevice. To model the directivity of our transducers, we utilized the piston model [37, 34] as a good approximation to
the pattern supplied by the manufacturer’s datasheet4 . Ourtransducers are designed to operate at a central frequency of
25kHz, and our control technique sweeps the frequency of asine wave randomly between 24kHz to 26kHz in steps of 1Hz,
every 0.45ms. To simulate multiple signal sources, different
random seeds are used in the generation of random frequency
sweeping of each source. To simulate a planar jammer, we
took the 3×3 array design by [46, 47], which features 9 transducers in a 3 × 3 planar grid. For the ring-layout, the transducers were placed in a diameter of 11cm. Our simulation
runs on a 96kHz, i.e., larger than the Nyquist rate for 25kHz.
Lastly, our simulation does not account for reflections.

each individual transducer Pt (p) = ∑s∈S Pt,s (p). And theaverage far field generated over time can be computed as
the root qmean square of the contribution of each time step
1
2
P(p) =|T | · ∑t∈T Pt (p) .
We simulated a total of 0.4s (roughly the average durationof a human spoken word [10]) with 13.573ms time gaps in
between each sample, up to 1-meter radius around the jammer. To simulate a moving jammer, we update the positionand orientation of each transducer at each sampled time stepand simply repeat the aforementioned process. To simulatea small movement, we rotated all transducers by 15 degrees
in 400ms – this depicts a relatively small microgesture of thewrist turning right.
Results

We performed four 3D simulations that suggested that a wearable jammer might outperform existing, planar or stationary,jammers. These are all depicted in Figure 4. For the sake ofvisual clarity, we plot only a 90◦ range of a 2D cross-sectionof the power distribution centered around the jammers.

a 1 signal w/ 9 transducers
in a planar layout

w/ 9 transducers
b 1insignal
a ring layout

1m

0.5m
blind spot
(weak jamming)

0m

0.5m

1m

0m

0.5m

1m

c 9 signals w/ 9 transducers d 1 signal w/ 9 transducers
in a moving ring layout
in a ring layout
1m

Given our transducer’s model, the complex acoustic pressure
Ps,t (p) contributed by each transducer s at a given position p
and time t is computed as:
Ps,t (p) =Pre f
2 · J1 (k · r · sinθ )d(p, ps )·) (1)· fs (t −d(p, ps )k · r · sinθ
c0

100
90
80
70

Simulation algorithm

Our simulation algorithm is based of Morales et al. [37] andMarzo et al. [34]. Let S be the transducers in a jammer, witheach transducer s ∈ S. Transducers are modeled as a pistonsource of radius r = 8.2mm. Let T be the time sampled in
the simulation, with each time step t ∈ T . Pre f represents
the transducers reference pressure; k is the wavenumber (k =ω/c0 ); d(p, ps ) is the distance between the transducer andthe point; θ is the angle between the transducer’s normal andthe point; J1 represents a Bessel function of the first kind, andfs (t) represents the signal transmitted by s at time t.

soundpressurelevel (dBA)

60

0.5m

50
40

0m

0.5m

1m

0m

0.5m

1m

30

Figure 4. Our simulations depict how different transducer layouts radiate around the simulated device. We found that, when moving in space,a wearable jammer outperforms stationary jammers.

Simulating planar jammers

Figure 4(a) shows a simulation of a planar jammer; this is
the design used in all known microphone jammers. We observed a rather limited angle coverage around the jammer,
suggesting that planar jammers are mostly directional. From
this insight, we decided to explore non-planar layouts.
Simulating ring-layouts

The total far field generated by all the transducers at time
t can be computed as the summation of the contribution of
3 http://sandlab.cs.uchicago.edu/jammer
4 Ultrasonic transducer (NU25C16T-1), Jinci Technology.

http://www.jinci.cn/showgoods/736.html

We simulated a ring-layout with 9 transducers. The result is
depicted in Figure 4(b). We observed that, when compared toplanar layouts, it radiates in all directions, with stronger components in the horizontal plane aligned with the transducers.
However, we also observed the appearance of the blind spots

One way to mitigate blind spots is to utilize a large numberof out-of-phase sources. For instance, if one scales up to 9
independent signal generators it would limit phase collisions
and thus reduce blind spots. We simulated this configurationand depicted it in Figure 4(c). We observed a smooth radiation pattern around the center—ideal for jamming. However,this approach drastically increases the number of components
required to manufacture this design, e.g., 9 signal generatorsand 9 amplifiers (one per transducer). This approach is thus,
highly impractical for a wearable implementation, both in its
hardware footprint and its power consumption.
Blurring blind spots via movement

Thus, the ideal wearable implementation would find a way
to mitigate the blind spots using only one signal source andone amplifier. Figure 4(d) demonstrates the power of making a jammer into a wearable. A wearable will move inspace alongside the user’s body. To simulate movement, we
turned the jammer by 15 degrees during the 400ms of thesimulation—as would occur when the user’s wrist would turnto the right slightly. The result, depicted in Figure 4(d), isa smooth radiation map, containing almost no blind spots.
We took this as the blueprint for our wearable jammer implementation. In the following laboratory experiments, we will
empirically confirm these simulation results.
EXPERIMENT#1: ANGULAR POWER DISTRIBUTION

In this experiment, we measured the angular power distribution (i.e., the power emitted at different angles) of our wearable device in comparison to our aforementioned planar 3×3
jammer and the commercially available i4.
Experimental setup

We utilized three jammers in this study: (1) The i4 (fromAmazon.com, $799) consists of two perpendicular rows of ultrasonic transducers, five transducers on the side and two on
the top. From our spectral analysis, the i4 operates at the low
end of ultrasonic frequency (20-24kHz), which allows its signals to travel further with slightly less power drop but unfortunately produces some disturbing audible sounds, likely due
to signal leakage in its transducers resulting from the 20kHz
signals. This device weighs 380 grams and consumes 4.2Wof power. When measured directly at the transducers (witha sound pressure meter), its loudness is around 92.4dBA. (2)The planar jammer is an array of nine ultrasonic transducers in a 3 × 3 configuration. We built this jammer following [46, 47]; this device uses precisely the same transducers
and amplifier as ours. The planar jammer used in this study
operates at 25kHz±1kHz (the same signal as our device) andis completely inaudible. Similarly to [46, 47], this is not a
stand-alone device and its power supply and circuitry are notintegrated. When measured directly at the transducers, its
loudness is around 92.6dBA. (3) Our wearable jammer wasanimated by a simple mechanical contraption. To move ourbracelet, we used a servo motor. We programmed the servo
to move 15◦ in 400ms, which is similar to slight wrist twist
if the device was worn by a user. When measured directly at
the transducers, our device’s loudness is around 92.3dBA.
To measure the angular power distribution of all three devices, we placed the jammers on a table, one at a time. We
measured all angles from 0◦ to 180◦ around the jammers at
a distance of one meter, in steps of 5◦ . To obtain an accurate power measurement, we utilized the HT-80A sound level
meter, which includes a well-calibrated microphone. Whenmeasuring our moving wearable, we took the average of theminimum and maximum power measured at each angle.
Results

The angular power distribution measured for our wearable
jammer, planar jammer and i4 are shown in Figure 5. We
found that our device provides a wide-spread angular coverage (M = −3.3dBA, SD = 1.6dBA), while the existing jammers are highly directional (planar jammer: M = −19.2dBA,
SD = 8.5dBA; i4: M = −17.0dBA, SD = 6.8dBA).
Furthermore, in the case of a planar jammer or the i4, even
within the angular sector of [0◦ ,40◦ ], a subtle angle changeof 2◦ leads to a 5-10dBA drop in their jamming power. This
uneven distribution is due to the aforementioned blind spot
problem [33]. Instead, the power of our wearable jammer has
no dramatic drops across all angles, as the movement helps toblur out the blind spots.
0
wearable-5
relative power (dB)between transducer pairs (zones where their signals cancel
each other out). These well-known blind spots are a key
disadvantage of any multi-transducer jammer [33]; a microphone placed within a blind spot is unlikely to be jammed
since the jamming signal intensity is weak.

-10
-15
-20
i4

-25
-30 (front)
0°
20°

planar

40°

60°

80°

(rear)
100° 120° 140° 160° 180°

Figure 5. Real-world measurements of the jammer’s angular coverage,
in terms of the signal power as the jammer-to-microphone angle α increases from 0◦ to 180◦ , normalized by the maximum power of each jammer. The distance between the jammer and the microphone is kept at
1m. Angular coverage of the wearable jammer under movement. Jammer is 1m away from microphone.

EXPERIMENT#2: JAMMING SPEECH RECOGNIZERSFor an end-to-end evaluation of jamming effectiveness, wemeasured the ability of state-of-the-art speech recognizers to
extract text from recordings of microphones jammed with ourwearable or the baseline devices.
Experimental setup

We tested the jamming effectiveness of three jammers: ourwearable device (animated by the same apparatus as in theprevious experiment) and two baseline devices (the planar
jammer and i4). We tested the jamming at multiple angles
from 0◦ to 180◦ , in steps of 10◦ and always 1 meter away

To create a comparable experiment across multiple devices
and angles, we cannot rely on a human speaker. Even a
trained public speaker that would not make any pronunciation mistakes, would still introduce confounding variables inour measurements as their voice would not be perfectly replicable across multiple trials, i.e., its loudness (dBAs), its direction, its timbre, and so forth. Therefore, we utilized prerecorded speech and played it back using a speaker (JBL GO,
frequency response from 180Hz-20kHz). Our speaker wascalibrated so as to play the pre-recorded human speech at astandard sound level of human conversation (55-66dBA measured at 1m away according to [40]). Lastly, the recorded
speeches used in our experiment were ten 1-minute long sentences taken, at random, from the LibriSpeech dataset [18],which is commonly used by speech recognition researchers.
For each trial, we played back the pre-recorded speech via
the speaker and recorded it with the smartphone’s microphone. Then, we fed these recordings into the IBM Speech to
Text [52]—a popular speech recognizer.
To compute the effectiveness of a jammer, we take the output of the recognizer and compare it to the transcript of eachsentence in the dataset (ground truth). This results in the percentage of the words that were incorrectly transcribed by the
text-to-speech; this is denoted as Word Error Rate (WER) andis a common metric in speech processing.

100%

wearable90%
80%
word error ratefrom our jammer. This experiment used the built-in microphones of a Nexus 6 and a Xiaomi Mi 6. For the sake of visual clarity, we depict only the most conservative result, i.e.,the device that best evaded our jamming–the Nexus 6.

i4

70%
60%
50%
40%
30%
20% (front)
0°
20°

planar

40°

60°

80°

(rear)
100° 120° 140° 160° 180°

Figure 6. Word error rate (WER) of speech recognition for jammingwith our wearable, planar jammer and i4. We found that the WER for
planar and i4 dropped drastically after 90◦ , while our wearable maintained a constant jamming effect >87%.

By contrasting the output of the text-to-speech when fed the
jammed recording vs. when fed the clean recording, we observed that most words became unrecognizable. Yet, somewords slipped through and were recognized, such as “space”.
jammer off

jammer on“now to bed boy”

“it”

“it is late and I go myself within a short space”“space”“most of all robin thought of his father what
would he council”

Figure 7. Examples of recognized sentences in clean speech case withperfect recognition and jamming case with our wearable jammer (WER
98.6%). Blank indicates nothing was recognized.

Results

Our results are depicted in Figure 6. We found that ourwearable device jams more effectively in all directions (M =96.59% WER, SD = 3.97%) than the existing devices (planar
jammer: M = 38.89% WER, SD = 21.72%; i4: M = 57.55%
WER, SD = 35.04%). Since we did not measure much difference between the measurements obtained from the two different smartphones, our results depict an average of both. Furthermore, note that even without jamming, no text-to-speechsystem is perfect. In our experiment, we measured that in
the absence of jamming the IBM Speech to Text had a WER
around 30% for the smartphone.
Moreover, we observed a similar pattern to the angular power
distribution found in the previous experiment. As depicted in
Figure 6, both the planar jammer and i4 exhibit WER drops
at 30◦ and 60◦ , around their blind spots. On the contrary,
our wearable jammer maintained a high WER throughout themeasured angles. Furthermore, we observed a severe drop in
WER, for planar jammer and i4, when the microphone wasplaced more than 90◦ away from the jammer (planar jammer:
M = 26.30%, SD = 2.16%; i4: M = 26.14%, SD = 2.07%;
our wearable: M = 97.92%, SD = 3.40%). First, this confirms that existing jamming approaches are highly directional.
Secondly, it confirms that our approach is effective even when
not pointing directly at the target device.
To exemplify the effectiveness of jamming with our wearable,
we depict in Figure 7 three short sentences from our dataset.

EXPERIMENT#3: JAMMING MICROPHONES COVEREDBY EVERYDAY MATERIALSAs we observed in our last experiment, our wearable jammer
has a wide angular coverage. Thus, it affords jamming even
without the user needing to point to the target microphone.
This feature allows it to also jam hidden microphones that theuser might not be aware of. In this experiment, we evaluatedwhether this type of ultrasonic jamming is effective when the
microphone is covered with a variety of materials, as it would
be typical of a hidden microphone (e.g., in industrial espionage [17, 28]).
Experimental setup

We repeated our previous experiment (same apparatus), except we this time covered the microphones with different materials. In particular: a plastic bag (0.2mm thick Polyethylene), a plastic box (1mm thick Polypropylene), a paper sheet(from a 20lb set), a paper tissue (3-ply tissue), a cardboard
box (3mm thick), a cloth (i.e., a cotton T-shirt), and two windshields (one fur and one foam) typically used in professional
audio recordings. Additionally, we also recorded a baseline
with no blockage applied.
Results

The results of the average WER are depicted in Figure 8. We
found that the paper tissue, paper sheet, foam windshield andcloth had little impact on jamming performance, resulting inan WER of 99%; in other words, our jammer was able to jam
microphones hidden by these materials and only 1% of thewords were correctly transcribed by the text-to-speech recognizer. Conversely, in the absence of our jammer, the text-tospeech recognizer recovered more than 60% of the words.

word error rate

100%
80%

jammer off

60%

jammer on

40%
20%
0%

no blockage

plastic card- plastic fur
paper cloth foam paperbox
board bagscreen tissue
screen sheet

Figure 8. Speech recognition results when the microphone is covered up
with various objects.

On the other hand, if the microphone was covered by a plastic
box or cardboard box, we observed that the jamming performance dropped considerably to WER 41.01% and 46.76%,
respectively; in other words, our device did not jam throughthe plastic box nor the cardboard box. In these two conditions, we also observed an increase of the WER even in
the absence of jamming up to 38.13% and 40.29% respectively. To sum it up, materials such as paper, cloth and foam
have little impact on jamming performance, while thicker ormore complex blockage materials (e.g., plastic box, fur windshield, etc) decrease the jamming performance. This result is
not a limitation of our wearable design but a limitation of
acoustic jamming in general, since ultrasonic waves are reflected/absorbed differently from those at the audible spectrum for a given material. Therefore, practitioners and consumers should be aware of such limitations, and a more indepth investigation of materials accordingly to their resonant
properties and acoustic impedance is required.
USER STUDY

In our earlier experiments, we focused on controlled laboratory experiments that validated the jamming effectivenessof our wearable jammer. In our final study we, instead, aim
to understand whether wearing our jamming bracelet impacts
one’s feeling of privacy. This study was reviewed and approved by our ethics committee (IRB19-0927).
Study design

Participants engaged in group conversations that lasted four
minutes. Neither the topic nor the volume of the conversations was controlled. We asked participants to speak one at atime (otherwise the speech recognizer cannot make sense ofit) and to not disclose any personal or sensitive information.
During the group conversation, participants wore our privacy
bracelet one at a time. They were asked to exchange thebracelet every minute, so that all could try it for an equal period. We recorded the conversation using four different commodity smartphones handed to each of the participants at thestart of the study. We used the audio from all smartphones’recording for speech recognition. After the conversation was
conducted, participants were presented with a transcript of the
speech recognition (for the smartphone that they had during
the study). After reading the transcript, they were asked torate how much they felt that the bracelet had protected theirprivacy on a Likert scale (1-7). Lastly, note that the baselineof this study is implicit, as participants have a recollection ofwhat they discussed in the group conversation and can judge
how much the effect of the jammer influenced their perception of privacy. This study design does not allow us to repeat
a non-staged conversation without the jammer nor were weinterested in measuring actual word error rate (as we did that
already in our previous controlled experiments).
Participants

To ensure that the English language level of each participant
did not negatively reduce the fidelity of the speech recognizer,
the candidates for this study were asked to read aloud sample
sentences. Candidates who got over 70% accuracy were invited to participate in the study. As a result, we selected 12
participants (aged 18-26 years old; four self-identified as females and eight as males) from our local institution for thisstudy. Ten of the participants had used some measure ofprivacy protection before, such as a laptop webcam cover,browser anti-tracking extensions, incognito mode, or VPN
service. None of these participants had previously used a microphone jammer.
Apparatus

We used our jamming bracelet. We utilized four smartphones
to record the conversation (models: Samsung S9+, Samsung
S7, plus the aforementioned Nexus 6, and Xiaomi Mi 6).
Lastly, we again used IBM’s speech recognizer.
Results

Participants rated the feeling of privacy induced by thebracelet as M = 5.4 (SD = 1.1). This result, coupled with
their positive comments, which we discuss below, suggested
that the bracelet provided a sense of protection for therecorded conversation. While the wearable jammer did not
jam the microphones completely in all recordings, the overwhelming majority of the transcripts of the four-minute conversations had only a dozen of mostly erroneous words.
When asked about their experience with the wearable jammer, most participants stated that they felt the bracelet was"definitely blocking out most words". Participants also notedthat in certain cases specific words still made it through, suchas the word facebook (P3). Three participants commented
that the bracelet is bulky but not uncomfortable (P7, P8, P12).
Two also added that while at the start, the bracelet was noticeable, once they focused on conversation, they "forgot about it"(P4) or "stopped feeling odd about wearing it" (P2).
Two participants (P10 and P8) added that they felt more protected either when wearing the bracelet or by simply seeing
others wear it. To this, P8 added that at the current size thedevice would not be discreet enough to jam without others
being unaware that you are doing so. Some participants (P5,P2, P1) noted that they would have liked to better understandthe range of the bracelet’s efficiency.
Lastly, all twelve of the participants stated that they will use
the bracelet again in the future. When asked specifically
about the kinds of situations they would use it for, they noted,
for instance: discussing private matters with their doctors(P1), discussing banking information (P6, P7, P10), talking
to their employers (P9), or to strangers that joined a private
conversation (P4).
DISCUSSION

Our experiments and user study provided insights into the advantages of a wearable microphone jammer. We found in
our experiments that a wearable jammer in a ring layout islikely to outperform stationary jammers or jammers with planar layouts. Furthermore, we found that our jammer actually provided participants from our user study with a sense ofincreased privacy against eavesdropping microphones. Yet,
there are a range of questions and limitations that we believe
are relevant to address to move the field forward.

we use randomly changing signals, which are hard to predict
and cancel out; and, (2) the motion of the user’s gestures andmovements is also hard to predict, making it also extremely
hard to cancel out these moving signal sources. Furthermore,
to make it even harder to perform noise canceling of the jamming signals, one could even design signals that exhibit cadence patterns similar to human voice.
SafetyOur proposed system uses ultrasonic frequencies in the
25kHz range, while the upper limit frequency that the human ear can hear is around 15k-20kHz. The U.S. Occupational Safety and Health Administration (OSHA) warns that
audible subharmonics can be harmful at intense sound pressures of 105 decibels or above [48]. Therefore, we ensured
our jammer did not surpass this threshold. We measured thesound pressure of our bracelet directly at the transducer andfound that its maximum sound pressure is below 92dB, wellwithin the aforementioned safety limits.
Unintentional and selective jamming

Limitations of our experiments

While we designed our studies to be as insightful and exhaustive as possible, it is simply not possible to test out the
jammer against an infinite amount of existing microphonebased devices. Therefore, one must take into account thatwhile our jammer was extremely effective against the microphones we used, these word rate errors cannot be easily
generalized to other devices. Furthermore, our transducers
are placed around the user’s arm in a circular layout, which
decreases its vertical coverage. In a preliminary experiment(using the apparatus of our experiment#2) we found that ourdevice provides a vertical jamming of over 97% (WER) up to75◦ ; however, the jamming drops at 90◦ (precisely on top of
the bracelet) to 75.54% (WER).
Non-linearities of microphone hardware

One speculative question is whether the non-linearity of today’s microphone hardware is just a transient artifact of today’s devices. We believe non-linearity is likely permanent
for the foreseeable future, because the MEMS microphones
used for smartphones and voice-based smart devices are designed for low-cost and small form-factors [30, 50, 12].
Counter-attacks to our wearable jamming

It is possible that attackers might craft exploits to circumvent
our wearable jammer. That being said, the most likely attack would be noise canceling techniques intended to cancelout the jamming signals. To provide some validation against
this attack, we de-noised the microphone recordings of ourjammed signal over our speech library (same as in our Experiment#2) using two methods: (1) the deep neural network(DNN) denoising method from Rethage et al. [45], and (2)the widely used Wiener filter [44]. We observed no improvement in the denoised speech (WER 99.64% for the DNNbased method, and 100% for the Wiener filter), when compared to the original jammed speech audio (WER 99.64%).
We believe that these current de-noising techniques will be oflimited effect because of two key factors of our design: (1)As with any of the current ultrasonic jamming techniques (notonly wearable jamming), it is possible that a jammer could accidentally jam legitimate microphones if these happen to bewell inside the jamming range, including one’s own smartphone, hearing aids or emergency response devices. Morework is necessary to understand the impact of ultrasonic signals on these devices and to design workarounds.
Similarly, a user cannot selectively jam devices using ultrasound jamming: e.g., a user cannot choose to avoid jamming
their own smartphone while still jamming another device. On
this limitation, our approach does provide more control thanexisting stationary jammers. Stationary jammers, once activated will jam their entire range, requiring the user to walk all
the way to the jammer to disable it. In our case, users can control the jammer’s behavior by simply touching the bracelet.
Moreover, moving forward, one would expect that adding intensity control to the wearable jammer might allow users totune the jamming range.
Future form factors

While we found that our device outperformed existing jammer approaches, it is still larger than a typical bracelet. We
believe our prototype offers a great blueprint towards a lowcost and ubiquitous microphone jammer. We expect this toinspire other wearable jammer designs, such as necklaces,
earrings or even clothing.
CONCLUSIONSWe proposed, engineered and validated a wearable microphone jammer that is capable of disabling any microphones in
the user’s surroundings, including hidden microphones. Our
wearable jammer takes the shape of a bracelet worn on theuser’s wrist and jams ubiquitously.
Our device is based on a recent exploit that leverages thefact that when exposed to ultrasonic noise, commodity microphones will leak the noise into the audible range. However,previous ultrasound jammers that also exploited this principle, required users to point the jammer to the target microphone. This was necessary as these devices were built basedon planar transducer layouts and were, therefore, highly directional. Unfortunately, this is impractical because it requires users to constantly worry and operate the jammer bypointing it to the surrounding microphones. Furthermore,this is sometimes impossible as users might desire to protect
themselves from hidden eavesdropping microphones.
Instead, we found that our device outperforms these state-ofthe-art jammers: (1) our wearable jams in multiple directions
since its transducers are arranged in a ring layout; and, (2) our
wearable jammer leverages natural hand gestures that occur
while speaking to blur out blind spots, which are the main
disadvantage of any jammer based on multiple transducers.
We validated these advantages by means of simulation andthree laboratory experiments.
Lastly, we conducted a user study with 12 participants thatrevealed that in a life-like situation participants felt that ourwearable protected their voice privacy. We believe our wearable provides privacy in a world in which more and more devices are constantly eavesdropping on our conversations.
ACKNOWLEDGEMENTS

Our research was partially funded by the National Science
Foundation, grants: CNS-1923778 and CNS-1705042. Any
opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do notnecessarily reflect the views of the National Science Foundation.
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