Digital Human Symposium 2009 March 4th, 2009
Sound Localization and Separation for Mobile Robot Tele-operation by Tri-concentric Microphone Array Yoko Sasaki ∗a ∗b
[email protected]
Satoshi Kagami ∗b∗c ∗a
[email protected]
Abstract The paper describes a tele-operated mobile robot system which can perform multiple sound source localization and separation using a 32-channel tri-concentric microphone array. Tele-operated mobile robots require two main capabilities: 1) audio/visual presentation of the robot’s environment to the operator, and 2) autonomy for mobility. This paper focuses on the auditory system of a tele-operated mobile robot in order to improve both the presentation of sound sources to the operator and also to facilitate autonomous robot actions. The auditory system is based on a 32-channel distributed microphone array that uses highly efficient directional design for localizing and separating multiple moving sound sources. Experimental results demonstrate the feasibility of interperson distant communication through the tele-operated robot system.
1.
Introduction
Tele-operated mobile robots are useful for visiting remote sites, visualizing their environments and communicating with distant people [1]. The operator can actively select the robot’s motion and attention through two vital robot functions: 1) audio-visual presentation to the operator, and 2) intuitive control of mobility. There is much research related to these topics, such as autonomy for mobility, online video/audio streaming, tele-existence, and so on. As one example, research on a robot to look around a museum has been reported [2]. In such a tele-operated robot, audio information is also important, such as detecting people talking to each other or to the robot, and machine beeping. If those events happen outside the camera view, the directional information of sound is very important to operate the system. Therefore, in this paper, we propose a presentation system of sound direction with omni-directional sensitivity and the separation of each sound source. There has also been an increasing amount of research related to the robotic audition system for human-robot communication. However, localizing and separating moving sound sources remains a challenging problem. A large amount of robotic audition systems [3] involve two microphones, emulating the human system; these systems rely on the difference in phase (IPD) and inten*a : School of Science and Technology, Tokyo University of Science *b : Digital Human Research Center, National Institute of Advanced Industrial Science and Technology (AIST) *c : CREST, Japan Science and Technology Agency (JST)
Simon Thompson ∗b∗a
[email protected]
Hiroshi Mizoguchi ∗a ∗b
[email protected]
sity level (IID) between the two microphones to localize one or two sound sources in a prescribed frequency band. Even though the research for developing ear-like auditory systems is rather challenging for moving robots, the use of a microphone array with many sensors increases the resolution of the localization procedure and its robustness against ambient noise. Recent work, such as [4], has proven that such an approach can be very efficiently applied to mobile robots and has consequently provided the motivation for us to choose an array-based approach. This paper mainly focuses on the auditory system of tele-operated mobile robots. We developed a 32-channel distributed microphone array providing highly accurate directional sound source localization and segmentation. This sound direction information is presented to the teleoperator to aid interaction. Experimental results verify our system’s capabilities for mobile localization and segmentation and demonstrate the use of sound source information in a tele-operating situation.
2.
Sound Localization and Separation Algorithm
Directional localization and separation of different pressure sound sources are achieved by integrating Delay and Sum Beam Forming (DSBF)[5] and Frequency Band Selection (FBS) algorithms[6]. The system detects multiple sound sources from taken in short period of time data. 2.1. Delay and Sum Beam Forming Aligning the phase of each signal amplifies the desired sounds and attenuates ambient noise. If we let Li be the distance of the focus to the i-th(i = 1, 2, · · · , M ) microphone and let Lmin be the minimum of Li , Equation(1) shows the delay Di for the i-th microphone and the synthetic sound wave s(t): Di =
Li − Lmin , Vs
s(t) =
M X
xi (t + Di )
(1)
i=1
where t is time, Vs is speed of sound and xi is the sound signal of the i-th microphone. The spatial spectrum (Figure 1) shows the sound pressure distribution for directional localization obtained by scanning the focus. Our system sets a focus 2 m distant from the array center and gets 180 data, 2 degrees shift. A band pass filter is applied to the input signal of each microphone. Higher frequency signals are susceptible to reflection, and a large microphone array is needed for lower frequency signals.
Signal input
DSBF phase
focus
Band pass filter
2m
Our system sets the frequency band from 300[Hz] to 3000[Hz], which covers everyday sound and especially the human voice.
This process rejects the attenuated noise signal from the DSBF-enhanced signal. The segregated waveform is obtained by inverse Fourier transform of Xas (ω). When frequency components of each signal are independent, FBS completely separate the desired sound source. This assumption is usually effective for human voice or everyday sounds in short time periods. 2.3. FBS-based sound directional localization We use FBS for direction-of-arrival estimation, as shown in Figure 2. The first step filters out the average signal of each microphone input(no delayed signal)using FBS and finds the loudest sound source from the spatial spectrum. When the frequency component of the average signal is higher than any DSBF-enhanced signal from each direction, the system filters out the spectrum of that frequency, rejecting omni-directional noise contains no sound sources. The second step filters out the first sound signal using FBS, and finds the second strongest sound source from the spectrum. When the frequency component of the DSBF-enhanced signal of the first sound direction is higher than that of other directions, the system filters out
localize 2nd strongest sound’s direction Direction [deg]
localize 3rd strongest sound’s direction
Continue filter for more than 3 sounds
Direction [deg]
Fig. 2 Sound Localization Process Power[dB]
Power[dB]
2.2. Frequency Band Selection DSBF is a very simple algorithm that enhances the desired sound source but cannot completely remove other signals. We apply FBS method after DSBF to detect multiple sound sources. FBS assumes that the frequency components of each signal are independent and is a kind of binary mask and segregates targeted sound sources from mixed sound by selecting the frequency components judged to be the targeted sound source. Our system uses DSBF-enhanced signals. The process is as follows: Let Xa (ωj ) be frequency components of DSBF-enhanced signals for target sound sources and Xb (ωj ) be those of noise sources. Selected frequency component Xas (ωj ) is expressed as in Equation(2): ½ Xa (ωj ) if Xa (ωj ) ≥ Xb (ωj ) Xas (ωj ) = (2) 0 otherwise
Filter out 2nd strongest sound signal
Direction [deg] Power [dB]
Fig. 1 Sound Localization by DSBF
Localize loudest sound’s direction
Power [dB]
Filter out loudest sound signal
b) Special spectrum
Power [dB]
FBS phase Filter out average signal
microphone array
a) Scan of focus
Calculate spatial spectrum
Frequency [Hz] Loudest sound’s direction
direction[deg]
a) 1st step
Frequency [Hz] Loudest sound’s direction
direction[deg]
b) 2nd step
Fig. 3 Spectrum Subtraction Process the spectrum at each frequency. Figure 3 shows the spectrum subtraction for the loudest sound sources, and summation of each frequency component obtains the spacial spectrum. For more than three sound sources, the system finds the third strongest sound source, and so on, after filtering out the second strongest sound signal. The method localizes multiple sound sources from the highest power intensity to the lowest at each time step, then continuously localizes multiple sound sources and separates individual sound sources during movement.
3.
Design of Microphone Array
This section describes the array design method, with focusing on the microphone arrangement determined by a beam forming simulation. The number of microphones is 32, because more sensors improve performance only up to a limit. The array must fit within a 50-cm-diameter board that is placed on the mobile robot. 3.1. Equation of Sound Pressure The equations for the beam forming simulation are as follows. Let C be the focus position, and Ric the distance from point C to each microphone. Each symbol is illustrated in Figure 4. Observed wave Qc at point C is written
Sound
R
2c
D
R
2
L
L
1
R
1c
0
0c
Concentric Array Y-AXIS [mm]
L
Y-AXIS [mm]
C
Ring Array
D
2
1
X-AXIS [mm]
Microphone
X-AXIS [mm]
Fig. 4 Beam Forming Simulation in Equation (3) as follows: Qc (t) =
M X Lmax + Ric − Li Li sin ω(t + ) Ric Vs i=1
(3)
Li /Ric means the damping coefficient for each microphone’s input. 1/Ric is the linear damping term for sound at point C and is normalized with Li . v u M M uX X t 2 Ac = ( Fic cos τic ) + ( Fic sin τic )2 (4) i=1
i=1
PM α = arctan Pi=1 M i=1
Fic cos τic Fic sin τic
(5)
From Equation (3), amplitude Ac and phase difference α, shown in Equations(4) and (5), are obtained. Fi c = Li /Ric and τic = (Lmax + Ric − Li )/Vs . The relative sound pressure level is described in Equation(6): SP LC = 20 log
Ac A0
(6)
where A0 is the amplitude at a reference point. This simulation sets the reference point 1 m from the array center. This means the sound pressure level at this point is 0 dB. In this simulation, sound frequency ω is used as a parameter and beam forming is simulated at individual frequencies. 3.2. Beam Forming Simulation The lower side-lobes and acute main-lobe perform accurate sound localization with an elevated S/N ratio. Side-lobes also caused erroneous judgment in directional localization of the sound source. The main-lobe width depends on the external diameter. The lower side-lobe arrangement is determined in the simulation by comparison to the ring array, which has the same microphone channels and outer diameter. We propose a tri-concentric array, reducing less side-lobes during beam forming. Figure 5 shows simulation results. The left column shows the formed beam of the ring array at each frequency and the right column is the formed beam of the concentric array. The array center was set at (2.5 m, 2.5 m) and the focus at 45◦ and 2.5 [m] from the array center. Comparison to the ring array indicates that the array we developed shows smaller side-lobes during
Fig. 5 Beam Forming Simulation Result
beam forming, especially from 700 to 1400 Hz. The S/N ratio of the proposed array is 7 [dB] higher than that of the ring array at a frequency range of 1 [kHz]. Thus, the concentric array provides high-quality localization and separation for multiple sound sources. 3.3.
32-ch Tri-concentric Microphone Array
Figure 6 shows the tri-concentric microphone array board we developed, which is installed on top of a mobile robot. The microphone array has 32 electret condenser
a) Microphone Array
b) Input Board
Fig. 6 32-ch Tri-concentric Microphone Array
microphones (Primo EM-100PT). It also has an amplifier on board and uses the Mic-input board for transferring signals to a computer. The specification of the Micinput board is shown in Table 1. It has 32 A/D converters (AD7680BRM) and can sample 32 data channels simultaneously. The sampling speed is 16 [kHz] in order to obtain audio signals less than 4 [kHz].
approach to have a high-level interface so that the human operator can simply indicate where to go with lowlevel autonomy for mobility in localization, planning, and control. Figure 8 shows some map, localization and path planning results. The entire cycle of these process takes about 150 ms for 15x12 m areas with a 5-cm grid cell. The motion control interface has a map and the operator uses a mouse to set goals.
Table 1 Specification of the Mic-input board Board size Input channels Interface Data transfer Sampling speed Resolution Power supply
4.
w=75, d=125, h=30 [mm] 32 IEEE-1394 isochronous 16 [kHz] 16 bits, programmable gain amp. +5 [V]
Mobile Robot for Tele-Operation
a) Front
b) Side
Fig. 7 Mobile Robot Platform The mobile robot platform we developed is 50 cm in diameter and 55 cm high (Figure 7). There are two passive caster wheels attached in the front and rear of the body. The robot can move on surfaces with about 1-cm height changes, and the maximum speed on a flat surface is about 1.5 m/s. It uses a 150 W Maxon DC motor with a harmonic drive gear having a reduction gear ratio of 30. The robot has a dual Xeon 3.0 GHz (SMP) operated using RTLinux for motor control (1-ms cycle PD control). The system is connected to the network by 802.11a wireless communication. It has three Li-ion batteries (24 V 6.5 Ah each) and can perform for about four hours of continuous operation. The robot carries two stereo cameras (Videre MDCS), a laser sensor (Sick LMS200), a 32-ch microphone array, a video conference system (Polycom VSX7000) and a touch-screen display. 4.1.
Autonomy for Mobility
We found empirically that avoiding collisions and generating smooth movement is a difficult task when a human operator drives the wheels directly, basically because of the lack of knowledge on the laser sensor for distance measurement and the robot body. We therefore took an
Fig. 8 Particle Filter Based Localization, Mapping and Path Planning Localization involves determining where a robot is within a known map. The framework behind the localization system developed in this work is that of particle filter localization [7]. The robot has a known map illustrated as black area in Figure 8. According to motion control or motor odometory information and laser sensor data, the predicted positions are evaluated against how their expected sensory view matches the current sensor view. Path planning is achieved using the method proposed in [8]. The method uses a 2D A* path planner [9] to generate the optimally shortest path through a 2D grid map to a goal location. A local subgoal is created along a two dimensional path (at about 5 m) and is used as the target for generating cubic and fourth-order curvature polynomials. These local trajectories are continuous in curvature and enable the robot to make smooth motions through the environment. Figure 9 shows an experimental result of 2D path plan and fourth order trajectory. This information is also shown on the robot control interface.
Fig. 9 Generated Trajectory From Path Planning Results
5.
Experiment
In this section, the performance of the microphone array interface and the experimental result of distant com-
munication by the tele-operated mobile robot are discussed. 5.1. Sound Detection Interface We first determined whether human beings detect the sound direction from stereo audio input. Sound is recorded stably by two omni-directional microphones at 33[cm] intervals on the robot. Figures 10 and 11 show the results of sound directional localization interviews for 10 people after 10[s] listening. The correct direction is also shown with vectors at the top of the graph. We ask subjects how many sounds they hear and which direction sounds come from. Results suggest that 50% detect 2 sound sources stably after 10[s] listening, but the direction of more than 3 sound sources is difficult to determine. Integrating audio and visual information helps detecting front or back, but the visual field is narrow and the directional detection of sound in a short-term data is difficult. female speech
does not consider the Doppler effect. Data length is 2048 point(128 ms) for each localization. Calculation speed is about 11 [Hz] on the robot computer. The system localizes reliably for up to 3 sound sources at certain instant calculations. The detection ratio of multiple sound sources are 94.2, 83.5, 67.8 and 56.4 % for 2 to 5 sound sources under stable conditions. Continuous speech set in 2[m] distant from the array center is used as sound sources for evaluation and the difference in each sound pressure level is less than 10[dB]. Figure 12 is a localization result for three sound sources.
Soft Soft
Loud
Microphone Array
Fig. 12 Sound Localization Interface
classic music
6 female speech classic music
5
count
4 3 2 1 0 0
45
90
135
180
225
270
315
360
direction[deg]
can’t answer
Fig. 10 Stereo Audio Sensing for Two Sound Sources
female speech
male speech
classic music
3 female speech male speech classic music count
2
The sound directional localization while robotic moving is evaluated. Figure 13 shows the robot path and speaker positions. The average speed of the robot is 0.3[m/s]. Three loudspeakers are set in the room, which reverberation time (T60 ) is 470 [ms] and ambient noise level (LA ) is 60[dB]. The sound sources are male speech(A), female speech(B) and classical music(C). Figure 14 shows sound directional localization results for music(position C). The sound pressure level is 62 to 67 [dB]. The robot position for each time instant is obtained by motion capture system (MotionAnalysis Eagle x 10, covered area: 5x5[m]) and the correct sound direction is calculated from robot positioning data. Figure 14 suggests that the system continuously localizes sound direction during movement. The average error is 2.65[deg] and maximum error is 13[deg] in the experiment. 0
path speaker
#
1 y [m]
-0.5
0 0
45
90
135
180
225
direction[deg]
270
315
360
can’t answer
Fig. 11 Stereo Audio Sensing for Three Sound Sources
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%
-1.5
$
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Fig. 13 Robot Path and Speaker Position Figure 12 shows the sound localization interface providing directional sound information to the operator. The microphone array detects the directions of sound sources from the highest power intensity to the lowest and indicates the directions from the left circle to the right, up to the third strongest sound source at each time interval. Brightness indicates the amplitude of sound, which normalized from no signal to the loudest sound at 0 to 1(white to black). The system assumes the moving speed is less than 0.5[m/s] for indoor application and
The system segregates multiple sound sources from mixed sound sources, enabling the operator to listen to a separated sound source when multiple sounds exist. Figure 15 shows the spectrograms of separated sound sources during movement. Experimental conditions are the same as for directional localization. The robot path and speaker position is illustrated in Figure 15 a). Two loudspeakers playing mixed sine waves are used as sound
error [deg]
direction [deg]
360 315 270 225 180 135 90 45
B
localization result mocap data
C
0.0
1.0
2.0
3.0 Time [sec]
4.0
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original_B
10 0 -10
separated_B 0
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10 15 time [sec]
20
25
0.5
1..0
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2..5 3.0 Time [sec]
3.5
4.0
4.5
original_C
Fig. 14 Sound Directional Localization during Movement
separated_C
1.0
sources. The frequency of sine waves is 700, 1030 and 1300[Hz] for source A and 850, 1270 and 1600[Hz] for source B. Figure 15 b) is the spectrogram of a microphone’s unprocessed input. c) and d) are separated signals of speakers A and B. At 1600[Hz] around 20[s], the result shows incorrect selection. The system separates individual sound sources from mixed sounds using continuously localized data during robot movement.
robot path(15-25[sec]) speaker
0
(TGSWGPE[=*\?
0.5
speakerA
y [m]
-0.5 -1 -1.5
1.5
2.0
2.5
3.0 3.5 4.0 Time [sec]
4.5
5.0
5.5
Fig. 16 Separation Results of Moving Sounds
showing the robot’s position and obstacles around the robot, and the operator setting a goal on the known environment map. The sound localization interface indicates arrival directions of the sound sources omni-directionally. An example of the interfaces provided to the operator is shown in Figure 17. The upper three windows are pro-
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speakerB
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b) Mixed sound
(TGSWGPE[=*\?
(TGSWGPE[=*\?
a) Robot path
6KOG=UGE?
c)Speaker A
6KOG=UGE?
d)Speaker B
Fig. 15 Spectrograms of Separated Sound Sources during Movement Figure 16 shows examples of waveforms of the separated sound sources which provided to remote robot operator. Two loudspeakers playing recordings of music and male speech, and a human speech are used for sound sources. The loudspeaker playing music remains stationary and other sources move around the system. The distance between the robot and the sound sources is 1 to 3 [m]. 5.2.
Distant Communication through the Mobile Robot This section describes the experimental result of distant communication using the mobile robot. The following three interfaces are provided to the robot operator: a video conference (TV) display showing the visual information in front of the robot, a robot control interface
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Fig. 17 Tele-Communication by Mobile Robot vided to the operator, and the picture at bottom left is a video clip of the robot side. An experiment was performed to confirm the capabilities of the system. Figure 18 shows videos and photos captured during the experiment. The operator controls the robot using the GUI display (upper left) and video conference display (upper right) from a distant place. Auditory information is given as arrival directions for each sound source. The elapsed time for this experiment is about 15[min] (including 2 [min] conversation), and the total length of robot movement is 254[m] there and back. The average speed of robot is 0.3[m/s]. First, the operator controls the robot going to the target room (b) by clicking on the desired location. There are no directive sound sources around the robot. When the operator calls a dis-
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Fig. 19 Research Center Floor with the Robot Path
tant person via the TV conference system (c), the auditory interface picks up from the forward direction, where a loudspeaker is attached to the robot. At the conversation stage (d), the interface shows the forward direction (loudspeaker) and on the other side, a person in alternate shifts or in parallel. When the operator and the distant person talk at same time, the interface shows the loudspeaker’s direction and the other person’s direction in the left 2 circles. In (e), the system obtains the call from behind while robot is moving. 5.3.
Discussion
The motion control interface and audio/visual presentation are important for tele-operated mobile robots targeting remote communication. The difficulty of remote motion control is indicated in [10], and we apply autonomous mobility function as a motion control interface. Omni-directional audio presentation is needed for intuitive operation in human around environment, because the robot’s visual field is narrow. As shown in Section 5.1, when there are more than 3 sound sources, the operator cannot detect sound by listening the sound more than 10[s]. We suggest that stereo audio interface is not sufficient for audio presentation due to the lack of active audition such as shaking the head to detect sound. The sound detection interface we proposed worked throughout the experiment. Online processing and robustness to environmental change during movement are key factors the success of this audio presentation. The interface presents up to 3 sound sources, which the system can detect reliable at each time interval. When there are more than 3 sound sources, it indicates each sound source alternately and the operator finds sources, because the sound pressure level changes dynamically even for continuous speech.
6.
Conclusions
This paper proposes a multiple sound localization and separation system as a sound presentation interface on a tele-operated mobile robot. We developed a 32-channel microphone array that produces lower side-lobes during beam forming. The design of the microphone array by beam forming simulation provides superior sensitivity omni-directionally. The proposed array increases the res-
olution of localization and its robustness against ambient noise. By using the DSBF and FBS algorithms, the system continuously localizes multiple sound sources and separates each sound source during movement under different acoustical conditions. Intuitive tele-communication through the mobile robot using autonomy in mobility and audio-visual presentation has been experimentally demonstrated.
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Operator’s display 1 (control interfaces)
Operator’s display 2 (TV conference)
Showing around the robot (video clip)
(microphone interface)
Close-up
a)
Going to the objective room
b)
“Hello!” (Operator calling)
c)
Talking each other
d)
“Bye!” (calling from the back)
e) Fig. 18 Videos and Photos of Experiment and GUI Displays. The Top Illustration (a) Describes Images in (b) through (e).
[10] Daniel Labonte, Francois Michaud, Patrick Boissy, Helene Corriveau, Richard Cloutier, and Marc Andre Roux. A pilot study on teleoperated mobile robots in home environments. In Proceed-
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