Acoustic detection prototype using GCC-PHAT on microcontroller
Nguyen Trung Hieu, Nguyen Hai Nam, Phan Hoang Anh
ACOUSTIC DETECTION PROTOTYPE
USING GCC-PHAT ON MICRO-
CONTROLLER
Nguyen Trung Hieu*, Nguyen Hai Nam*, Phan Hoang Anh+
*Posts and Telecommunications Institute of Technology
+VNU University of Engineering and Technology
quality, which is essential, are the sound source
localization technique.
Abstract: This paper presents an acoustic directional
detect device’s development and design based on the
Generalized Cross-Correlation PHAseTransform (GCC-
PHAT) technique. The topic’s primary goal is to build a
KIT that supports audio processing, including a 4-
channel Microphone array and a popular microcontroller,
STM32F103c8t6, embedded with GCC-PHAT algorithm
to estimate the direction and intensity of the sound source
captured. We also have tested, evaluated, and compared
the performance with a sound source localization device
on the market. The article focuses on data processing and
optimizing computational methods to enhance
performance while overcoming the limitation of the
microcontroller.
Many devices are capable of locating sound sources
that have been presented using different methods to serve
a variety of purposes in today’s world. One of them is
more specific noise source localization, such as
aeroacoustic measurements in wind tunnels, flying
aircraft, and engine testbeds[2]. Characterized by tests
aimed at evaluating and verifying an aviation vehicle’s
efficiency, the methods used in this field place a heavy
emphasis on accuracy and, in fact, use hefty hardware to
achieve the most accurate results. Among them are
deconvolution methods such as DAMAS[3], CLEAN[4],
LPD[5], beamformingbased methods like GIBF[6],
RAB[7] or others like SEM[8], SODIX[9], IBIA[10].
Another application of sound source locating is speech
recognition and speech separation[11] for human-machine
interaction applications. The most prominent example in
this area might be the ASIMO robot[12] using the HARK
framework[13] or the Hadaly robot[14]. Other
applications, such as multirotor-UAV[15], use the MUSIC
method or the IHRTF[16] algorithm for robotic heads.
However, one common feature of the applications
mentioned above is that they are all production prototypes
using tightly linked hardware and software. On the other
hand, heavy and expensive hardware is also a limitation
for new researchers entering this field. Realizing this,
STMicroelectronics released STEVALBCNKT01V1[17],
also known as the BlueCoin kit. However, at $80, this
device is not affordable for many people. Therefore, with
the idea of creating a simple, affordable acoustic direction
detection device, suitable for students, engineers, and
researchers to assist the learning and research process.
The topic content will present the device’s development
process and design to support the sound direction of
arrival (DOA) detection with a simple microcontroller’s
hardware platform and localization algorithm.
Keywords: Microcontroller, STM32, sound source
localization, GCC-PHAT, cross-correlation.
I. INTRODUCTION
Several studies have focused on developing
humancomputer or human-mobile interfaces in intelligent
environments to support user tasks and activities.
Acknowledgement of voice/sound location and direction
provides valuable information allowing for a better
understanding of user activities and interactions in those
environments, such as analyzing group activities or
behaviour, deciding who is the active speaker among the
participants, or determining who is talking to whom [1].
Along with a vibrant scientific and technological
revolution, the sound processing engineering field has also
experienced continuous development. A series of robots,
AI manufactured by research institutes worldwide, has
been announced with a great attraction. Their unique
feature that makes the whole world pay attention is the
ability to communicate and talk to people like tour-guide
robot, Siri (Apple), Cortana (Microsoft), or Sophia (the
first robot citizen of the world). In terms of technical
aspects, to get the ability to communicate with people, the
recognition of sounds and voices is indispensable in the
processing process. The identification technique’s quality
is of utmost importance and directly affects the entire
communication system’s quality. Many methods and
techniques developed to improve speech recognition
This paper is composed of 5 sections. Section II
summarizes the theoretical basis. The process of
optimizing hardware and software is presented in section
III. While section IV covers system design and
evaluationg, finally concludes are presented in section V.
II. THEORETICAL BASIS
One of the most common ways to estimate DOA is to
exploit the time-difference-of-arrival (TDOA) of the
sound wave to a microphone array. Its popularity base on
the fact that it can be modified to match the researcher’s
Correspondence: Nguyen Trung Hieu
email: [email protected]u.vn
Manuscript communication: received: 10/03/2020, revised:
11/23/2020, accepted: 11/30/2020.
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ACOUSTIC DETECTION PROTOTYPE USING GCC-PHAT ON MICRO-CONTROLLER
interest, such as one or two-dimensional localization of
single or multiple sound sources. But to understand the
basic principle, let’s look at an example on a pair of
microphones.
other. It is commonly used for searching a long signal for
a shorter, known feature. It has applications in pattern
recognition techniques, electronic tomography, averaging
techniques, decoding, and neurophysiology.
Figure 2. Cross-Correlation example
Consider two real-valued functions, f and g, differing
only by an unknown shift along the x-axis. One can use
the cross-correlation to find how much g must be shifted
along the x-axis to make it identical to f. The formula
practically slides the g function along the x-axis,
calculating the integral of their product at each position.
When the functions match, the value of (f ∗ g) is
maximized because when peaks (positive areas) are
aligned, they make an enormous contribution to the
integral. Similarly, when troughs (negative areas) align,
they also make a positive contribution to the integral
because the product of two negative numbers is positive.
Figure 1. Utilized feature example
In a free/far-field propagation model, as shown in Fig.
1, there is a sound source which is transmitting a sound
signal to two microphones 1 and 2. Consider the sound
wave that approaching the microphone pair is a planar
wave, which means there is an amount of time difference
of arrival (TDOA) between these microphones let us call
it tdelay. Therefore, there is a simple feature-to-location
mapping between tdelay and the DOA of the source.
For example, with the Fig. 2, the two sine wave
signals, f and g, have been deviated by 20 unit in the time
domain, their cross-correlation result has peaks located at
position -20 in time lag axis. This implied that if we shift
g sooner by 20 unit, we will get f.
V
sound t
delay
l
()
= arccos
= arccos
d
d
where θ is the angle created between the sound source
direction and the imaginary line between the microphone
pair; d is the spacing of the microphone pair; l is the
distance difference of arrival between Mic1 and Mic2,
which is constructed from the multiplication of speed of
However, this cross-correlation execution has been
proved to be very sensitive to reverberation and noise. In
these cases, the cross-correlation results are "spread" to
other TDOA regions, making it difficult to determine the
right time difference. On the other hand, these resulting
peaks are quite dull, causing in low certainty.
sound Vsound = 343.2(m/s) and TDOA as tdelay
.
However, with each obtained tdelay, there is another
DOA result that can be produced as θ’, which mirroring
the actual source. This ghost source appearance is
addressed as an ambiguity problem which has been
existed since the beginning of the overall binaural
approach. To avoid this, in section III-A, we will provide
a simple square array solution and how to extract the
incoming direction from it.
B. Cross correlation on frequency domain based on
GCC-PHAT
The researchers have proposed cross-correlation in the
frequency domain (CCF)[18] to solve the above problem
as an alternative to conventional crosscorrelation. This
technique gave a different result F-1 (CCF) with cross-
correlation in the time domain CCT, but it has been shown
that the positions of the peaks match each other.
TDOA processing mentioned in the article is an
effective way to solve the deviation between
microphones. Many algorithms, such as GCC, GCC-
PHAT, and DFSE, can effectively estimate TDOA.
However, we have chosen the GCC-PHAT method for
this research because the PHAT method has better
performance in Gaussian noise distribution and the actual
noise environment. GCC-PHAT has also required less
computational resources, which is essential when
executing on a microcontroller.
The primary purpose when doing cross-correlation
over the frequency domain is that we can apply some
weighting functions to the result; this technique is called
general cross-correlation (GCCF). So if this weight is
equal to the magnitude of the correlation itself, we will
normalize the magnitudes in GCCF, causing the inverse
Fourier transformation results to have peaks in the Dirac
delta function approximate form in the high correlation
TDOA. This normalization left the information about the
phase intact, so it is called phase transform (PHAT).
A. Cross correlation in time domain (CCT)
In signal processing, cross-correlation is a measure of
the similarity of two signal chains or like a function of the
displacement between one signal series relative to the
Y f Y f
Y f Y f
0 ( ) 1 ( )
GCC − PHAT f =
F ( )
()
0 ( ) 1 ( )
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Nguyen Trung Hieu, Nguyen Hai Nam, Phan Hoang Anh
The GCC-PHATF is a cross-correlation in the
frequency domain incorporate with phase transform. To
put this in perspective, we show the theoretical
comparison results (Fig. 4)[19] of two similar input
signals, which is considered an audio source with one is
delayed intentionally. Both graphs show the same
deviation peaks, but in the graph (b) we can easily see that
it has a sharper peak and is more recognizable.
Considering a more interesting case as the second
signal is an interfering component. Specifically, in the
follow-up investigation, the signal was virtually
experiencing reverberation with a known delay. The graph
below (Fig. 5) shows the cross-correlation results of both
methods in this situation.
Figure 5. Result difference between two method
in reverberation environment
In graph (a) with the conventional cross-correlation, it
was proved that it failed to separate the delay between the
echo and the actual signal, directly affecting the obtained
deviation’s quality. In contrast, with GCC-PHAT, we
separated two signals and obtained two separate sharp
peaks, and showed the signal’s exact delay (Fig. 5(b)).
GCC-PHAT distinguished them because it had sharp
peaks that did not spread to neighbouring peaks like time-
domain cross-correlation.
III. PROPOSED DESIGN
A. TDOA to DOA
As mentioned in section II, if we only use two
microphones or a microphone array lined up in a straight
line, we will be challenging to determine whether the
sound is coming from the front or the back of the
array(ambiguity). Therefore, to define TDOA on a plane
(specifically in this topic is the azimuth plane), we need to
have a microphone array made up of multiple microphone
pairs to form a parallel plane to the detecting one.
Thus, with the GCC-PHAT technique applied to the
system, the noise and echo problem have been resolved,
providing a more stable system and can be used in many
different environments.
To detect the sound source in a 1-dimensional plane,
we consider a set of 4 microphones placed at the four
angles of a square. Fig. 6 shows the layout of the
Microphones on the device. The distance between the
microphone pairs is a constant D, assuming that the sound
source is the only source, no noise. With a free/far-field
propagation model, the sound wave can be considered
planar, its direction creat with the sides of the square two
angles: α and β. The value l1, l2, indicates the distance
difference between two microphone pairs to the sound
source. Obtaining this difference, we can calculate the
angles α and β or the DOA of the source.
l
1
= arccos
D
Vsound 12
fsample D
Vsound
fsample D
34
= arccos
= arccos
= arccos
()
l
2
= arccos
Figure 3. GCC-PHAT transformation
D
Vsound 23
fsample D
Vsound
fsample D
14
= arccos
However, if we only use the angle α or β, the
ambiguity problem mentioned in section II begins to
emerge. So using the microphone array as above will
allow us to combine the resulting angle obtained from
each microphone pair to produce a final result as µ, with
Figure 4. Result difference between two method
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ACOUSTIC DETECTION PROTOTYPE USING GCC-PHAT ON MICRO-CONTROLLER
layout in fig. 6, we can see the results of the correlations
xc12 ~ xc34 and xc23 ~ xc14. Therefore, we add these two
cross-correlations together to get xc1234 and xc2314; the goal
is to reduce later calculation and, at the same time, reduce
the error of the cross-correlation. The Max blocks are the
blocks to find the highest peaks to obtain the
corresponding difference of l1234 and l2314. From there, by
some trigonometric formulas related to the microphone
array spacing D, we can calculate the incoming angle of
the sound µ.
sin
( )
cos
cos
( )
( )
tan =
=
( )
cos
( )
()
cos
( )
= arctan
cos
( )
C. Methods to enhance processing accuracy
Since the entire algorithm will be executed on a
microcontroller, we will encounter some limitations
that affect the overall system’s efficiency. For that reason,
in the research and development process, we have
performed some investigations, tests to find out about
these limitations and how to solve them, eventually
improve the performance.
1) DC Offset Removal
The DC Offset Removal technique is a technique to
remove the mean amplitude deviation from the coordinate
axis. In other words, the central horizontal axis of audio
signals we receive does not match with the horizontal axis
of the coordinate (Fig. 8).
Figure 6. Array layout of the proposed device
B. Overall algorithm
So, after calculating the cross-correlation between two
signals as mentioned in section II-B, the maximum value
of the cross-correlation function will indicate when the
signals are best aligned, or the time delay between the two
signals is determined by the maximum value or the
maximum angle of the cross-correlation.
With this information, it is possible to exploit and
calculate the incoming sound direction with a more
extensive microphone array system, as mentioned in
section III-A. From there, we created a block chart
representing the calculation process of the system as
follows.
Figure 8. The DC Offset problem
In particular, this error occurs because there is
always an electric compensation fixed somewhere in
the sound signal before it is converted from analog to
digital. These offsets are usually so small that it is not
noticeable, but it can become large enough to be a
problem on low-quality hardware.
There are many ways to solve this problem; the
simplest one is that the signals after ADC will be
subtracted with its average value using the Moving
Average method. As a result, the signal will align with
the horizontal coordinate axis.
Y ,
t =1
t 1
Figure 7. Overall algorithm of the device
1
()
S =
t
Y + 1− S ,
(
)
Fig. 7 is the block diagram of processing audio
signals captured from the microphones. In turn, each
microphone pair will be cross-correlated and determine
the deviations in time between them. The xcorr blocks
in the figure are cross-correlations; through the array
t
t−1
2) Signal interpolation
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Nguyen Trung Hieu, Nguyen Hai Nam, Phan Hoang Anh
Another limitation worth mentioning when using
lightweight hardware is that the analyzed data will be
discrete. Therefore, the resolution of the obtained result
will depend significantly on the number of samples taken.
However, with limited hardware, this number must be
carefully controlled to ensure the device’s performance.
Therefore, to increase the resolution of the results without
affecting the device, we decided to use the interpolation
method.
incoming angle and intensity. These two values will be
displayed to the user via the LED Ring module. Figure 11
is a picture of the audio direction detection device.
Figure 9. Interpolation process
After the cross-correlation is completed, we
interpolate immediately to optimize computing FFT and
IFFT. Moreover, with the above transform, we can cancel
the inverted Fourier transforms of the cross-correlation
block with the interpolation block’s Fourier transforms.
Figure 11. Acoustic detector prototype
B. Experiment and evaluation
To examine the device’s performance, we operate a
test with the sound source placed at ten random angles
(Fig.12), with each angle, the source play at different
distances, respectively. Table I contains the results
obtained from the device at each run. When analyzing the
experimental results, we can see that the device performs
relatively uniformly in all the angular cases on the
azimuth plane. With sound sources play at a longer
distance, the return error is more significant, which is
expected. At a distance below 40cm, the device produces
very accurate results at 1.47o to 2.40o of average error.
From the distance of 80cm-100cm, the accuracy of the
device is still very high with an average error of about 3o.
Figure 10. System design
IV. SYSTEM DESIGN AND EVALUATION
A. System design
The diagram in Fig. 10 describes the audio directional
detection device general model built on the
STM32F103c8t6 microcontroller. The audio signal is
sampled by the STM32F103c8t6 microcontroller from
four microphones that already incorporated an op-amp.
The microcontroller’s ADC samples the signal with a
sampling frequency of 12KHz to obtain a series of digital
audio signals. These are stored directly into BuffSize with
a capacity of 2048 samples right after the ADC
conversion is completed. DMA technique helps to reduce
the information storage process, allowing the data to be
calculated immediately. The signal through the processing
unit using the algorithms described above will result in the
Figure 12. Experiment setup
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ACOUSTIC DETECTION PROTOTYPE USING GCC-PHAT ON MICRO-CONTROLLER
Figure 13.Performance in comparison
To put this in perspective, we compare it with an
audio-source localization device, ST-BlueCoin, which is
developed and competing in the market also by
STMicroelectronics. The results showed the difference in
performance between the two in terms of accuracy (Fig.
13).
density. Signal filtering and interpolation have also been
added to improve accuracy. Survey results have shown the
relative accuracy of the device and its quality compared to
a previously developed device. The device is just designed
and presented as a prototype, so this opens up a lot of new
research directions for further development. For example,
we can extend the effective working range of the device in
terms of spatial dimension and distance. On the other
hand, we can also make more optimal use of the
hardware’s working capabilities, eliminate redundant
details, and reduce the physical size of the hardware and
the complexity of the program. Furthermore, the device
can act as a module in a higher purpose system such as
audio source tracking, separation and recognition.
At a distance of 10cm, both devices work well and
both give accurate results when the two graph lines
showing errors are low and almost identical. The
difference begins to appear at a distance of 20-40cm when
the BlueCoin device begins to show discrepancies in
calculating the direction of the sound. At a distance of 80-
100cm, the BlueCoin device is no longer stable while the
device we developed still retains high accuracy.
ACKNOWLEDGEMENT
Table 1. The results of the experiment
The authors would like to acknowledge the PTIT
Team lab of Posts and Telecommunications Institute of
Technology, Hanoi, Vietnam for their commitment and
support of the conducted research.
Distances (cm)
Angle
35
70
10
20
40
80
100
34.502
67.490
104.32
139.38
173.59
209.39
245.36
284.64
313.39
351.75
35.554
65.127
106.61
139.24
175.05
213.98
247.43
286.62
313.43
348.49
36.558
69.175
108.64
137.67
175.48
212.34
247.80
284.53
315.22
351.16
38.398
70.112
110.15
138.09
173.85
213.87
248.72
288.04
317.69
350.26
38.072
75.311
111.05
138.73
171.36
210.27
248.73
288.86
316.94
346.7
105
140
175
210
245
280
315
350
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V. CONCLUSIONS
We discussed the solution to detect the sound source
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Nguyen Trung Hieu, Received the
B.S. and M.Sc. degrees in electronics
and telecommunications, and the Ph.D.
degree in electronics engineering from
Posts and Telecommunications Institute
of Technology, Hanoi, Vietnam, in 2006,
2010, and 2018, respectively. From 2006
to present, he was with the Faculty of
Electronics Engineering, Hanoi, Vietnam.
He is currently Head of the Department of
Electronics and Computer Engineering.
His research interests include coding
theory, signal processing, communication
systems, IoT devices and systems, and
electronics design.
Nguyen Hai Nam, Received the B.S.
degree in electronics engineering from
Posts and Telecommunications Institute
of Technology (PTiT), Hanoi, Vietnam, in
2020. He is currently Team leader of
Signal Processing group, Department of
Electronics and Computer Engineering,
PTiT. His research interests include
signal processing, Microcontroller.
Phan Hoang Anh, Recieved the B.S.
degree in electronics engineering from
Posts and Telecommunications Institude
of Technology (PTiT), Hanoi, Vietnam, in
2019. He currently works in Department
of MicroElectro-Mechanical Systems and
Micro Systems, VNU University of
Engineering and Technology (VNU-UET).
His research in interests include signal
processing, robotics, automation.
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Ph.D. dissertation, Virginia Tech, 2002.
PHÁT HIỆN HƯỚNG ÂM THANH SỬ DỤNG
GCC-PHAT TRÊN VI ĐIỀU KHIỂN
Tóm tắt: Bài báo này trình bày quá trình nghiên cứu
và thiết kế một thiết bị phát hiện hướng âm thanh dựa
trên kỹ thuật tương quan chéo kết hợp biến đổi pha
(GCC-PHAT). Mục tiêu chính của nhóm nghiên cứu là
xây dựng một bộ KIT hỗ trợ xử lý âm thanh, bao gồm
SOÁ 04A (CS.01) 2020
TAÏP CHÍ KHOA HOÏC COÂNG NGHEÄ THOÂNG TIN VAØ TRUYEÀN THOÂNG
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