PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Visualization of Distribution of Room Acoustic Parameters by Using
Mobile Robot
Masashi UEHARA1; Naoki ISHIKAWA; Shigeki OKAWA
1Chiba Institute of Technology, Japan

ABSTRACTIn this paper, we designed a robot system to generate a map which shows acoustic information in a room.
A distribution of room acoustic parameters helps us understand acoustic environment; nevertheless it needs
measurement of impulse responses at multiple points that are time-consuming. To visualize a distribution ofroom acoustic parameters with efficiency, we utilized a mobile robot with SLAM (which estimates theself-localization and environment map simultaneously). The mobile robot leverages self-localization torecord measurement points and utilizes an environment map as a base to be plot acoustic information on. The
robot repeats measurement of impulse responses and moving all over the room. We extract parameters fromeach impulse responses then conversion this parameters to color. Finally, we plot the color information atmeasurement points on the environment map. We examined how visualized a distribution of room acousticparameters in an actual meeting room with enough accuracy. In result, the robot system generated maps ofroom acoustic parameters that are related to reverberation and speech clarity.
Keywords: Impulse response, Room acoustic measurement, Parameters mapping

1. INTRODUCTION
Measurement of impulse response (IR) leads to acquisition of room acoustic information thatincludes feature of reflection in a room. Some room acoustic parameters calculated from IRs areknown to correspond to psychological quantities. These parameters help us to understand how
reverberation sounds in the room. Moreover, necessity of acoustic measurements at multiple points issuggested (1). In some parameters, it is not possible to explain the acoustic characteristics of a room
only by measurement results at one point representative of the room.
However, room acoustic measurements need a lot of manual operations, e.g. to move microphones
and loudspeakers, record their positions, play measurement signal and record sound. Measuring at
multiple points in particular is time-consuming.
Meanwhile, a robotic system is often utilized for measurement (2, 3). This is attributed to arobotic system can record self-location and sensed data on automatically. The improvement of ROS(robot operating system) and computer performance enable us to use navigation techniques easily.
Our goal is to improve efficiency of acoustic measurement by adding mobility to measurement
equipment. It is meaningful to visualize the spatial distribution of room acoustic parameters for thepurpose of showing the benefit of efficiency of measurement. Thus we proposed and developed aroom acoustic parameters mapping system with mobile robot.

2. ROOM ACOUSTIC PARAMETERS MAPPING SYSTEM
2.1 Structure of the systemFigure 1 describes a structure of the acoustic parameters mapping system. This measurement
system is constructed from two parts: a measurement robot (shown as Figure 2) and an operation unit.
The measurement robot based on i-Cart mini mounts a condenser microphone (AT2020USB+) anda RGB-Depth camera (kinect for windows v1). A mobile robot enables to move sound receiving point.
i-Cart mini is a mobile robot platform which has differential-drive wheels for efficient mobility.
The operation unit has three functions: to send trigger that starts measurement, remote control of1s1126017jn@s.chibakoudai.jp

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the measurement robot and record sounds.

Figure 1 – System diagram of room acoustic parameters mapping system.

Figure 2 – Overview of the measurement robot.
2.2 IR measurement
Proposed system plays and records a Log-SS signal for IR measurement. The frequency ofLog-SS is swept logarithmically. Figure 3 draws a waveform of the measurement signal.
A measurement signal emitted from loudspeaker is consist from 5 cycles of Log-SS. Synchronous
addition of the middle 3 cycles of the 5-cycle signal increase the signal-to-noise ratio of themeasured signal. Moreover convoluting Inverse Log-SS signal and the synchronous added signal
obtains IR.

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Figure 3 – waveform of measurement signal Log-SS
2.3 Room acoustic parameters
Plotted information on the acoustic parameters distribution map are C 50 [dB] and early decay time(EDT) [sec]. Each parameter is calculated from measured IRs filtered with 1 octave band; the center
frequency is 500 [Hz]. A C 50 is given byஶହ଴‫ܥ‬ହ଴ ൌ ͳͲଵ଴ ቆන ‫ ݌‬ଶ ሺ‫ݐ‬ሻ ݀‫ݐ‬൘න ‫ ݌‬ଶ ሺ‫ݐ‬ሻ݀‫ݐ‬ቇ(1)ହ଴଴

, where ‫݌‬ሺ‫ݐ‬ሻ is an instantaneous sound pressure at time ‫[ ݐ‬ms]. In addition, an EDT is derived
from decay curve ‫ ܵۃ‬ଶ ሺ‫ݐ‬ሻ‫ ۄ‬shown followsஶ௧‫ ܵۃ‬ଶ ሺ‫ݐ‬ሻ‫ ۄ‬ൌ න ݄ଶ ሺ‫ݐ‬ሻ݀‫ ݐ‬െ න ݄ଶ ሺ‫ݐ‬ሻ݀‫ݐ‬
଴

(2)

଴

, where ݄ሺ‫ݐ‬ሻ is IR. The EDT is determined by multiplying the time taken for 10 dB attenuationon the decay curve by six. These acoustic parameters are known effect to conversation in livingrooms (4).
2.4 SLAMSLAM (simultaneous localization and mapping) of the measurement robot is powered byRTAB-Map (5) that can output a map as 3D point cloud data. SLAM which is widely used fornavigation tasks provides information where robot is and a map of the surrounding environment.
Proposed system utilizes the self-localization result to record measurement point and draw acoustic
information on the mapping result.
Proposed mapping system draw circles filled a color converted from room acoustic paramet ers on
the map generated by SLAM.

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3. MEASUREMENT EXPERIMENT
3.1

Condition of experiment

Figure 4 – The experiment environment and the measurement robot.

Figure 5 – The condition of experiment environment.
(left: the room layout, right: flowchart of the experiment)
Measurement experiment was perfor med at a meeting room shown as Figure 4, 5 (left) in Chiba
Institute of Technology. The 20markers indicate points where the measurement robot moves to for4614

examination of position detection accuracy.
Figure 5 (right) shows a flow of measurement experiment. In these processes, we moved themeasuring robot by remote control. At first, it is necessary to obtain approximate room shape for
accurate self-localization and mapping. Once before acoustic measurement, we moved themeasurement robot evenly in the room to generate a map. Note that all steps shown as Figure 4(right) took about 20 minutes.
3.2 Accuracy of localization detection
The markers shown in Figure 5 (left) indicate a point where the measurement robot moves to for
examination of position detection accuracy. Figure 5 shows measurement steps and errors of detected
position intervals and marker intervals. From figure 6, position detection accuracy is enough forvisualization because errors are less than 210[mm]; this value is narrower than the width of a typicalchair.

Figure 6 – errors of position detection by measurement steps.
3.3

Acoustic parameters mapping

Figure 7 – visualization result as room acoustic parameters maps. (left: C50, right: EDT)
Figure 7 shows distribution maps of room acoustic parameters. The results obtained arereasonable as they are consistent with the general finding that the longer the ED T, the lower the C 50 .
It was found that there are relatively large differences in acoustic parameters even at adjacent seats.

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4. DISCUSSION
We showed that it is possible to generate a map about room acoustic parameters by automatic
location information acquisition with SLAM. This finding, while preliminary, suggest that adding
mobility to acoustic equipment is meaningful for visualization of measurement results.
The distribution of room acoustic parameters drawn on environment map clarified that thehearing differs between the seats. This supports that suggested necessary of location information inprevious studies of visualization of sound field.

5. CONCLUSIONSIn this paper, the goal was to assess the efficacy of adding mobility to acoustic equipment formeasurement. Thus we proposed using mobile robot to move a sound receiving point and visualizedmeasurement result as a distribution maps.
Further studies of presentation of acoustic information need to be undertaken because we could
not discuss validity of 2D distribution expression.

REFERENCES
1. Hisaharu Suzuki, "Visualization of sound field for room acoustical research," The journal of theacoustical society of Japan, vol. 64, No. 7, pp. 399-404, 2008.
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Acoustic Impedance Mapping using Mobile Robots", 2018 IEEE Conference on Decision and Control;
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3. Atsushi Watanabe, Jani Even, Luis Yoichi Morales, Carlos T. Ishi, "Robot-assisted Hammer Sounding
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4. Hayato Sato, Masayuki Morimoto, Kaori Kobuki, "The effect of reverberation sound on conversation inliving rooms," The journal of the acoustical society of Japan, vol. 66, No. 11, pp. 541-551, 2010.
5. IntRoLab, "RTAB-Map Real-Time Appearance-Based Mapping," Retrived June 1, 2019, from(http://introlab.github.io/rtabmap/).

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