Tongaraas:
Tongs for Recognizing Littering Garbage
with Active Acoustic Sensing
Koki Tachibana

tachibana.koki.ti0@is.naist.jp
Nara Institute of Science andTechnology
Ikoma, Nara, Japan

Daiki Mayumi

mayumi.daiki.mb9@is.naist.jp
Nara Institute of Science andTechnology
Ikoma, Nara, Japan

Yuki Matsuda

yukimat@is.naist.jp
Nara Institute of Science andTechnology
Ikoma, Nara, Japan

Takamasa Kikuchi

rb22308r@osaka-pct.ac.jp
Osaka Metropolitan University
College of Technology
Neyagawa, Osaka, Japan

Keiichi Yasumoto

yasumoto@is.naist.jp
Nara Institute of Science andTechnology
Ikoma, Nara, Japan

Kaito Isobe

kaito.isobe@iis.ise.ritsumei.ac.jp
Ritsumeikan University
Kusatsu, Shiga, Japan

Hirohiko Suwa

h-suwa@is.naist.jp
Nara Institute of Science andTechnology
Ikoma, Nara, Japan

Kazuya Murao

murao@cs.ritsumei.ac.jp
Ritsumeikan University
Kusatsu, Shiga, JapanCan

TobaccoPaper

Petbottle

Plastic

None

Figure 1: Tongaraas: IoT Tongs for Littering Garbage Recognition with Active Acoustic Sensing

ABSTRACT
Littering has developed into a serious environmental problem. However, the actual situation of litter and the results of litter pickup
activities are not organized as information. Therefore, the objectiveof this research is to grasp the distribution of the type and locationof litter comprehensively. To achieve the objective of this research,
we have proposed a method for recognizing litter using an acoustic

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IoT ’22, November 7–10, 2022, Delft, Netherlands© 2022 Association for Computing Machinery.
ACM ISBN 978-1-4503-9665-3/22/11. . . $15.00
https://doi.org/10.1145/3567445.3571112

sensor on a smartwatch worn on the wrist and a method for recognizing litter using a small camera mounted on tongs. However, inthese studies, there were limitations in the range of litter type estimation, lack of recognition accuracy, and privacy issues. To solve
the above problem, we propose a litter type recognition system,
named Tongaraas, that combines active acoustic sensing with tongs.
In the evaluation experiment, we built the litter type recognition
model for six categories of litter. The evaluation results showed
the models, which were trained with dataset collected by single
person and three people, perform at F-value of 0.978 (SVM) and
0.849 (LightGBM), respectively. It suggests it is possible to estimate
with common litter type recognition model, although there is acertain level of negative effects due to the individual difference.

CCS CONCEPTS• Human-centered computing → Ubiquitous computing; •Information systems → Location based services.

IoT ’22, November 7–10, 2022, Delft, Netherlands

KEYWORDS
Smart city, Littering, Active acoustic sensing.
ACM Reference Format:
Koki Tachibana, Yuki Matsuda, Kaito Isobe, Daiki Mayumi, Takamasa Kikuchi,
Hirohiko Suwa, Keiichi Yasumoto, and Kazuya Murao. 2022. Tongaraas:
Tongs for Recognizing Littering Garbage with Active Acoustic Sensing . In
Proceedings of the 12th International Conference on the Internet of Things (IoT’22), November 7–10, 2022, Delft, Netherlands. ACM, New York, NY, USA,
7 pages. https://doi.org/10.1145/3567445.3571112

1

Koki Tachibana et al.

be affected by hands holding the tongs, individual differences canlead to give negative effects for recognition models. To investigate
this concern, we evaluated performance of the model trained withthree person dataset. The results showed the model with LightGBM
performs at F-value of 0.849 with LOGO-CV. It suggests it is possible to estimate with common recognition model, although there isa certain level of negative effects due to the individual difference.

INTRODUCTION

Littering is a global problem, adversely affecting the health ofwildlife and causing death. Solving this problem is an urgent task.
Yamane et al. [17] point out that there are three approaches tosolving the littering problem: (1) continuous litter cleanup, (2) installation of trash cans and signs encouraging people not to litter,and (3) government intervention. However, the actual situation oflitter and the results of litter pickup activities are not organized as
information. Therefore, anti-littering measures currently rely on experimental rules. In addition, litter pick-up activities are conductedindividually in each region and community and are not coordinated.
In response to this situation, there are services [14] that survey
the distribution of litter for a fee, and several municipalities are
actually operating such services. However, these surveys require
investigator to travel around the region, which makes them difficult
to cover and sustain spatiotemporally. This research aims to realize
a sensing technology that enables the collection of information
on litter types and locations with high spatiotemporal coverage.
Previous studies have explored approaches to apply the participatory sensing framework [2] to people who routinely pick up litter.
Specifically, we have proposed a method for recognizing litter using
an acoustic sensor on a smartwatch worn on the wrist [15] anda method for recognizing litter using a small camera mounted on
tongs [16]. However, the former method requires the user to rap
litter by hand to generate a sound, and the later method also has
the problem that image recognition accuracy is degraded in theearly morning when the surroundings are dark.
Therefore, we focused on active acoustic sensing as an approachto realize new litter recognition. Active acoustic sensing is a methodof estimating the state of an object by attaching a microphone and a
loudspeaker to the surface of the object, emitting a specific acoustic
signal through the loudspeaker and propagating it inside the object,
and analyzing the frequency of the response signal obtained by
the microphone. An example of the application of this method tocutlery shows that this method can recognize foodstuffs that comeinto contact with the cutlery [11]. we propose a system, named
Tongaraas, that recognizes litter types by employing active acoustic
sensing to tongs (Figure 1).
In the evaluation experiment, we constructed two types of datasetscollected by single person and three people targeting five types oflitter (cans, tobacco, paper, plastic, plastic bottles, and nothing) andbuilt the litter type recognition models for each dataset. For single
person dataset, the results showed the model with SVM performs
at F-value of 0.978 through leave-one-group-out cross-validation(LOGO-CV). It suggests active acoustic sensing will be an effective
clue for distinguishing types of litter. Because acoustic signal might

2 RELATED WORK
2.1 Research of litter recognition
There are many studies on recognizing litter. Several research
projects [1, 3, 6] use machine learning to identify litter using video
data collected from a fixed camera. However, a fixed camera canonly collect information on the type and location of litter, so thismethod can’t collect information on a wide range of litter. Although
it is possible to place fixed cameras in the entire area where wewant to collect litter information, it is not realistic considering thenumber of fixed cameras to be installed.
Inoue et al. [7] studied the relationship between the distribution of litter and the shape of the banks by manually identifying
litter along the riverbanks. Pirika Inc. is developing Pirika [13], asocial networking application for volunteer litter pickers. Volunteer
litter pickers take pictures of litter with their smartphones andupload them. However, the method of manually recording types
and locations of litter [7] requires time-consuming input of litter
information and does not efficiently collect types and locations oflitter. Similarly, using Pirika [13] to upload photos of individual
pieces of litter is time-consuming for volunteer litter pickers.
Pirika Inc. has also developed Takanome that analyzes the type,
quantity, and location of litter captured on video and plots it ona map when a person takes pictures of litter on the road witha smartphone [14]. Hong, Fulton, Kraft et al. [4, 5, 9] use robots
to recognize types and locations of litter. These researches not
only collect information on types and locations of litter, but also
assume that the litter is actually picked up by robots. However,Takanome [14] has higher operating costs because of the labor
required to survey distributions of litter. In addition, the method
using robots [4, 5, 9] incurs the cost of operating the robots.
To solve the above problems, we have proposed a litter type
recognition system using an acoustic sensor in a smartwatch wornon the wrist [15]. However, this system has the problem that it needs
to generate sound by rapping litter by hand and that it cannot recognize objects that do not generate sound even if they are rapped.
For example, no sound is generated by rapping a littered cigarette.
In addition, we have proposed a method for recognizing litter typeusing a small camera mounted on tongs [16]. Although this system
can recognize litter such as tobacco, image recognition accuracy
is degraded during dark hours such as early morning, when litter
pickups are often conducted. In addition, there are concerns about
privacy issues arising from acoustic data collected from the smartwatch’s microphone and image data collected by the tong-mounted
camera.

Tongaraas: Tongs for Recognizing Littering Garbage with Active Acoustic Sensing

2.2

Position of this researchThis study aims to expand the range of recognizable objects andreduce environmental dependence, which are the remaining challenges in our previous research [15, 16], by incorporating active
acoustic sensing as a new approach to litter recognition.

3

TONGARAAS

In this section, we propose a new litter type recognition system, Tongaraas (IoT Tongs for Littering Garbage Recognition with Active
Acoustic Sensing), which incorporates active acoustic sensing into
tongs.

3.1

Overview

In this paper we focus on that litter and tongs come in contact witheach other when picking up litter. In addition, litter is composed ofmaterials with different acoustic properties (metal, paper, plastic,etc). For example, cans and pet-bottles are cylindrical, whereas
plastic are bag-shaped. These differences in shape are thought toappear in the frequency response. The materials of each type of litter
also differ in the same way. Cans are mainly made from aluminum or
steel, while pet-bottles are made from polyethylene terephthalate.
We think that this difference in material properties is similarlyshows up in the frequency response. Based on the above, we believethat active acoustic sensing can be adapted to the task of estimating
the type of litter.
Tongaraas can classify types of litter such as tobacco, which
could not be classified by the method of classifying the type of litterby rapping litter [15]. Also, because Tongaraas don’t use an image
recognition method to determine types of litter [16], there is nochange in accuracy even when the surrounding area is dark. In
addition, the use of sensing based on acoustic signals in the ultrasonic domain has the advantage of protecting privacy. In summary,
if Tongaraas can sense types of litter, it is expected to expand therange of litter types, improve recognition accuracy, and protect
privacy.

Contact
speaker

Computer

Research on object recognition by active
acoustic sensing

There are many studies on object recognition using active acoustic
sensing. Ono et al. [12] have proposed a touch recognition techniqueusing this active acoustic sensing. This is a method to recognize thegrasping state of an existing object by attaching a contact microphone and a contact speaker to the object and acquiring differencesin the way the object is touched. Adiyan et al. [10] proposed amethod to infer the position of contact and gestures on the bodyby resonating the skin surface with low-frequency ultrasound andreceiving it at different points on the body. Nishii et al. [11] havedeveloped fork-shaped and spoon-shaped device capable of active
acoustic sensing and proposed a method for recognizing foodstuffs
that come into contact with it. Thus, it has been shown that active
acoustic sensing can be used to recognize objects in contact, andthe technology has been used for various applications.

2.3

IoT ’22, November 7–10, 2022, Delft, Netherlands

Sweep signal
generation

Contact
microphone

outputAudio signal
collection
Label

input
Featuregeneration

ML training
Estimation
model

Litteringrecognition

Estimation result

Tong for picking garbage

Figure 2: System architecture of Tongaraas

3.2

System architecture

An overview of Tongaraas is shown in Figure 2. The system consistsof litter picker tongs fitted with a contact loudspeaker that emits
acoustic signals and a contact microphone that collects the acoustic
signals, the computer with sweep signal generator section, acoustic
signal collector section, feature extractor section and litter type
recognition section.
The procedure for litter type recognition is as follows.
(1) A sweep signal whose frequency varies with time is generated by the sweep signal generator in the computer andemitted from the contact speaker.
(2) The acoustic signal is collected by a contact microphone (theacoustic signal varies depending on the type and shape ofthe litter in contact with the tongs).
(3) From the collected acoustic data, the frequency band of the
Sweep signal is extracted for feature extraction.
(4) Recognize the type of unknown litter in contact with thetongs using the litter type recognition model.
In the following sections, sweep signal generator section, acoustic signal collector section, feature extractor section, and litter type
recognition section are described in detail.

3.3

Sweep signal generation and acoustic signal
collection

The sweep signal generator section generates a sweep signal (alsocalled a chirp signal) whose frequency varies linearly from 20 kHz
to 40 kHz. This frequency band was determined with reference toprevious research papers [12]. When the sweep signal is played backrepeatedly, recognition may be affected by the impulse noise generated by the large change in frequency at the repetition break (themoment when the frequency changes to 40 kHz and then returns
to 20 kHz). In this system, this problem is solved by increasing thefrequency to 40 kHz and then decreasing the frequency, as shownin Figure 3. The sampling frequency is 96 kHz in order to convert
the analog sweep signal transmitted through the tongs into a digital
signal for acoustic signal collection.

IoT ’22, November 7–10, 2022, Delft, Netherlands

Koki Tachibana et al.

freqency [kHz]

40

20

0

Discontinuity

Discontinuity

Discontinuity

200

400

elapsed time [ms](a) Sweep signal with simple repetitions

LenovoThinkPad X1 Carbon
Sweep signal generation,
Acoustic signal collection

Tip of tongs

40

Steinberg UR22mkII

freqency [kHz]Audio interface

20

0

200

400

(b) Sweep signal in this paper

elapsed time [ms]Figure 3: Sweep signal generation method

UxcellPiezoelectric device
contact speaker, microphone

Table 1: List of feature values
Feature
MFCC
ChromaMelspectrogram
Spectral contrast
Total

3.4

Number of dimensions
104
12
128
7
251

Feature extraction and litter type
recognition

As a pre-processing step, a frequency band between 20 kHz and
40 kHz is extracted from the collected acoustic data using a bandpass filter to eliminate acoustic signals in a frequency band different
from the Sweep signal.
Next, features are extracted from the acoustic data. The list of features to be extracted is shown in Table 1, with a dimension numberof 251. These features are commonly used in acoustic recognition;
therefore, they are used in our system. In this system, each feature
is extracted using the librosa1 library in Python.
Mel-frequency cepstral coefficient (MFCC) is a feature that is
obtained by performing a fast Fourier transform (FFT) on sounddata and then an inverse discrete cosine is applied to transformthe output through a mel filter bank. Chroma is a feature that is asuperposition of all the components of the same scale in different
octaves, reduced to the 12 components of the chromatic scale within
an octave. Melspectrogram is a spectrogram created after the FFT
and the frequency is converted to the mel scale. Spectral contrast is afeature that is obtained by applying a FFT, passing the result throughan octave filter bank, processing to detect and extract the peak, andfinally transforming using the Karhunen-Levé method [8].

4EXPERIMENT

To evaluate the effectiveness of Tongaraas, acoustic data of multiple
litter objects are collected, and litter type recognition models areconstructed. We also investigate the difference in performance
between the models built with single person’s dataset and multiple
people dataset.
1 https://github.com/librosa/librosa

Figure 4: Tongaraas data collection experiment setup

4.1

System setup

The setup of the data collection system used in the evaluation experiment is shown in Figure 4. A Lenovo ThinkPad X1 Extreme
Gen3 (CPU: Intel Core i7-10750H, RAM: 64GB, OS: Windows 10)
is used as the computer for sweep signal generation and acoustic
signal collection, and sound input/output is performed via a USBconnected audio interface Steinberg UR22mk. The contact speakermicrophone used was an uxcell piezoelectric device (35 mm diameter, 0.3 mm thick), attached to the tip of the tongs as shown in theupper right.

4.2

Target litter categories

In this paper, a total of six categories were targeted: five representative types of litters (cans, tobacco, paper, plastic, and pet-bottles)
and a state in which the litter is not pinched. Figure 5 shows appearance of each litter. The 20 litter objects are prepared for each littertype. Even if they are the same type of litter, their materials andshapes are greatly different. For instance, cans differ in the capacity(185mℓ, 300mℓ, 355mℓ), the shape (pull-tab or cap type, and degreeof concavity), and the material (steel, alminium).

4.3

Data collection and evaluation procedures

Using the system described in Section 4.1, the participants collectacoustic dataset for each litter object as explained in in Section 4.2.
The acoustic signal is recorded while the tongs are pinching thetarget litter. The data will be collected for 12 seconds per a time,and extract 10 seconds data by removing first and last 1 second for
excluding acoustic noise at the start and the end of recording. Fromthe cut-out acoustic data, split the data into 1 second segments andgenerate 10 samples of acoustic data in total.
In this paper, we made two types of datasets for evaluating thefollowing two points. First, we aim to confirm the feasibility of litter
type recognition based on an active acoustic sensing approach, witha dataset collected by a single person. Second, because acoustic
signals might be affected by hands holding the tongs, individual

Tongaraas: Tongs for Recognizing Littering Garbage with Active Acoustic SensingCan

IoT ’22, November 7–10, 2022, Delft, Netherlands

Petbottle

TobaccoPlastic

Paper

Figure 5: Photographs of litter used in the evaluation experiment (5 types, 20 objects each)differences can lead to give negative effects for recognition models.
Hence, we aim to investigate the effects of individual differences
on recognition models, with a dataset collected by multiple people.
Data collection and evaluation procedures for both datasets arefollowing.

will be split into the 5,700 samples of training data (6 litter categories × 19 litter objects × 5 pinching ways × 10 samples) andthe 300 samples of testing data (6 litter categories × 1 litter object
× 5 pinching ways × 10 samples). By changing testing data, therecognition model will be built and evaluated 20 times in total, witha setting where the training data does not include data from thesame litter object.

Evaluation of a model built with acoustic data from single person.
We collected the acoustic dataset of 20 litter objects for each target litter category by single person. Regarding each litter object,
the acoustic data will be collected five times with changing thepinching way. Consequently, we acquired the dataset with 6,000
samples of acoustic data (6 litter categories × 20 litter objects × 5
pinching ways × 10 samples). As a evaluation method, we adopted
Leave-one-group-out cross-validation (LOGO-CV). The method of
splitting dataset into training and testing data for LOGO-CV isshown in Figure 6. Specifically, the one object from 20 litter objects
for each litter category will be taken and used as testing data, andremaining will be used as training data. For each trial, the dataset

Evaluation of a model built with acoustic data from multiple people.
We collected the acoustic dataset of five litter objects for eachtarget litter category by three people. Regarding each litter object,
the acoustic data will be collected three times with changing thepinching way. Consequently, we acquired the dataset with 2,700
samples of acoustic data (3 people × 6 litter categories × 5 litter
objects × 3 pinching ways × 10 samples). As a evaluation method,
we adopted LOGO-CV same with case of single person.
To build litter type recognition model with 251 dimensional features (Table 1) as input and six litter categories as output, by using
LightGBM which has been shown higher classification performance

IoT ’22, November 7–10, 2022, Delft, Netherlands

Can

Koki Tachibana et al.

TobaccoPaper

Petbottle

Plastic

None

Group 1

①

①

①

①

①

Group 2

②

②

②

②

②

Group 6

⑥

⑥

⑥

⑥

⑥

Group 20

⑳

⑳

⑳

⑳

⑳

Test data of
LOGO-CVfor 1st batch(300 samples)

1,000 samples
in total

Test data of
LOGO-CVfor 6th batch

10 samples x 5 pinching way = 50 samples

Figure 6: How to split training and test data for LOGO crossvalidation. The data from same garbage is belonging to only
one group.

Figure 7: Average confusion matrix of the model built withdataset of single person (LightGBM).

than other machine learning methods in our previous study [15].
To compare performance with common machine learning methods,
we additionally selected SVM and Random Forest. Also, we applied
Hyperopt2 for hyperparameter tuning.

4.4

Result and discussion

The evaluation results are shown in Table 2. When the model wasbuilt using dataset of single person, the best performance is showedwith SVM: the average accuracy was 0.978, the average precision
was 0.979, the average recall was 0.978, and the average F-value
was 0.978. Compared with the baseline method [16], our proposed
method shows better performance though it uses lighter-weightmachine-learning algorithms. Then, when the model was built
using dataset of three people, the best performance is showed with
LightGBM: the average accuracy was 0.855, the average precision
was 0.871, the average recall was 0.855, and the average F-value was
0.849. Here in after, we will describe detailed results and discussions
in the case of LightGBM which shows the best performance formultiple people dataset.
Figure 7 and Figure 8 show the average confusion matrix of amodel built with dataset of single person and three people, respectively.
For the model built with the dataset of a single person, Table 2
and Figure 7 showed it performs with quite high F-value of 0.955. It
suggests that if the user used a model built with their own acoustic
data, it is possible to recognize types of litter with high performance.
From this result, we have confirmed the approach in this paper
employing active acoustic sensing is feasible for recognizing litter.
Then, for the model built with the dataset of a three people, Table 2 and Figure 8 showed its performance dropped to the F-value of
0.849, but it is confirmed relatively high performance is kept. It suggests This result suggests we need to take into account the effects ofindividual differences for building robust recognition models. One
possible reason for the lower performance is that different users
hold different parts of the tongs. To solve this problem, it might benecessary to design affordance to unify the position of grasping the2 https://github.com/hyperopt/hyperopt

Figure 8: Average confusion matrix of the model built withdataset of three people (LightGBM).

tongs, and filter the specific frequency band for removing effects
by the user’s hand.
To investigate the reason for low performance in the case of
three people, we focused on the worst case among trials of LOGOCV whose average F-value was 0.634 as shown in Figure 9. From
Figure 9, we found out that the performance of “none” and tobacco
are relatively high but the others are low. This trend was observedin many other trials. In the case of “none,” it is reasonable the reason
for the high performance because the tongs are not grabbing litter.
In the case of tobacco, we considered that because the shape of
tobacco is smaller than others and has uniformed shape, the F-valueof tobacco became higher (Figure 5). Compared with them, otherlitter types have various shapes and sizes. It might cause confusion
for the recognition models. Especially, the shapes of cans are varied
as shown in Figure 5. As can be seen from Figure 5, most of cansare crushed. Therefore, we believe that the cans are not making
good contact with the surface of the tong tips due to the degree ofconcavity of the cans, which is affecting the F-value. We consider
that the problem can be solved by modifying shape of tong’s tip

Tongaraas: Tongs for Recognizing Littering Garbage with Active Acoustic Sensing

IoT ’22, November 7–10, 2022, Delft, Netherlands

Table 2: Result of evaluation
DatasetSingle person

Multiple people

MethodLightGBM
SVM
Random Forest
Baseline [16]*aLightGBM
SVM
Random Forest

Accuracy
0.956
0.978
0.945
0.855
0.850
0.843

Precision
0.959
0.979
0.951
0.905
0.871
0.869
0.852

Recall
0.956
0.978
0.945
0.902
0.855
0.850
0.843

F-value
0.955
0.978
0.945
0.903
0.849
0.844
0.839

*a The model has been built using MobileNet.

REFERENCES

Figure 9: Confusion matrix of a model built with dataset ofthree people at the worst trial (LightGBM).

for capturing audio signals effectively. For example, we attach adeformable gel to the surface of the tongs tips so that the tongs canmake contact with concavity of the cans.

5

CONCLUSION

This paper proposes, a litter type recognition system, named Tongaraas, which employs active acoustic sensing to tongs. In the
result, when the model was built using acoustic data from single
person, the average F-value was 0.978 (SVM). From the result, it
was suggested that if the user used a model built with acoustic
data collected by himself, it might be possible to recognize typesof litter precisely. Also, when the model was built using acoustic
data from three people, the average F-value was 0.849 (LightGBM).
From the result, when acoustic data was collected by more than one
person, we found that individual difference give negative effects tothe performance of recognition. As future work toward improving
performance, we will explore to recognition model which is robust
to individual differences by filtering frequency band affected due
to human body (hand) and litter shape differences by modifying
shape of tong’s tip for capturing audio signals effectively.

ACKNOWLEDGMENTS
This study was supported in part by JST PRESTO under Grant No.
JPMJPR2039 and JPMJPR1937.

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