1. 23I SVC23rd
International Congress on Sound & Vibration
10-14 July 2016Athens,Greece
1
IMPLEMENTATION OF LAB VIEW SIGNAL PROCESSING
PLATFORM FOR ACTIVE NOISE CONTROL IN ACOUSTIC
WAVEGUIDE
Аlexander А. Igolkin, Аrthur I. Safin, Мaxim V. Balyaba, Мichael А. Ermilov,
Pavel I. Greshnyakov, Andrey G. Troshin
Samara State Aerospace University, 34, Moskovskoe shosse, Samara, 443086, Russian Federation
e-mail: artursafin1988@gmail.com
In the paper example of the active noise control system based on Lab View platform is pre-
sented and discussed. To check capability of the Lab View platform for Active Noise Control
the model of the short acoustic wave guide was utilized. The noise source is modeled by dy-
namic loudspeaker while another loudspeaker used as compensational one. Built-in DSP algo-
rithm based on FxLMS has implemented for evaluation the performances of the active noise
control related to duct system and acoustic wave guides. ANC performances were tested using
multi-tone and single tone signals which are typical for ducted fan noise spectrum. The capabil-
ity of Lab View platform in Active Noise Control technology is addressed and revealed. The
noise reduction index is measured as a difference between two sound pressure levels when ANC
on and off and equal to 15 dB of reduction.
1. Introduction
The era of active noise control started in 1936 when Paul Lueg German engineer granted the pat-
ent [1] which describes an active noise control idea to suppress the primary sound wave by its copy
shifted to 180 degree in phase. This effect is so-called the “destructive interference” is used in engi-
neering literature. The proposed technology could not be implemented in practice for a long time
because at first sight a very simple idea requires the huge development of electronics, signal proces-
sors, effective and not expensive electro acoustical components like power D-class amplifiers, loud-
speakers with effective permanent magnets, reliable and low cost electret microphones.
Besides those developments the basic mathematical theory of adaptive filtering with compensa-
tion of delays in electro acoustic equipment was required.
2. Active noise control methods – history and background
Theoretical basis for active noise control has been developed by Nelson & Elliot from Institute
of Noise and Vibration Research, Southampton (United Kingdom) [2]. Algorithms for active noise
control were proposed by Sen Kuo & Dennis Morgan [3]. Numerous practical applications for ac-
tive noise control and its industrial implementations were made by Hansen & Snyder University of
Adelaide, Australia) [4], [5].
In last decade several companies successfully launch on the market place the active noise cancel-
lation headsets. The headset with active noise control option provides additional from 15 to 20 dB
noise reduction in aircraft cabin or another vehicle. HARMAN International developed and demon-
strated active noise control system for engine order cancellation in the car cabin as a part of the pro-

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International Congress on Sound and Vibration
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ject HALOSONIC. The system utilizes standard loudspeakers and amplifier of car audio system and
does not require any additional components except of installation the error microphones which do
not substantially increases the total cost of the technical solution.
“Silentium” Co. Ltd from Israel proposed the various usage of active noise control for ventilation
and air conditioning systems. It also should be mentioned here the project SAAB-2000, where im-
plementation of active noise and vibration control technology providing the global cancellation of
the BPF tone and its second harmonics of propeller noise by 18 and 15 respectively, using 48 chan-
nel control system.
Authors [6] М. S. Gasparov, М. Yu. Kharitonov proposed active noise control system for the
duct. The system utilizes sensors of particle oscillatory velocity produced by Microflow Technolo-
gies Company and comprises the acoustic feedback suppressor based on low pass filter and FPGA.
The disadvantage of the system is an implementation unique sensor of particle oscillatory velocity
which price could make the building of commercial system more complicated.
It should be noted that active noise control methods are low frequency technology which able to
suppress noise in the frequency range below 500 – 1000 Hz. This is fundamental physical limitation
determined by size of sound wave length. Indeed the wave length determined the quite zone size as
it was proven in [1]. It can be easy figured out using simple estimation that for 1000 Hz the wave
length size in the air with speed of sound 340 m/s is equal to 0.34 m. To enlarge the size of the quite
zone the increasing number of control channels is required since one control channel with loud-
speaker physically covers the area of one half of wavelength. Reduction of the wave length while
frequency is increases makes limitation for size of the control loudspeaker in multi-channel system.
In multi-channel systems the control loudspeaker clusters should be arranged with separation dis-
tance equal to half of wave-length to extend the quite zone. It is obvious that control loudspeaker
size for high frequency should be less than 0.34 m to perform the control loudspeaker array. This
fact creates the limitation for cancellation of the low frequency components since the loudspeaker
efficiency in a low frequency range depends on its enclosure volume. The purpose of this research
is designing the prototype of the active air-borne noise control for ventilation and air conditioning
system of industrial and dwelling buildings.
3. Practical embodiment of the active noise control system (ANC)
3.1 FxLMS algorithm of adaptive filtering – theoretical background
FxLMS algorithm was proposed by Sen Kuo & Dennis Morgan in reference [3]. The block-
diagram of the algorithm is depicted in Fig. 1. The peculiarity of the FxLMS algorithm is an addi-
tional block S(z) which allows to compensate delay of the reference signal in electro acoustic path
due to power amplifier and control loudspeaker.

3. The 23rd
International Congress on Sound and Vibration
ICSV23, Athens (Greece), 10-14July 2016 3
Figure 1:Block-diagram of FxLMS. P(z) – primary path, x(n) –reference signal, x’(n) – filtered reference
signal, d(n) – noise signal , e(n) – error signal , y(n) – compensation signal anti-noise, S(z) – electro acoustic
transfer path, W(z) – adaptive filter, LMS – Least Mean Square algorithm.
The algorithm is belong to adaptive digital filtering algorithm family with minimum least mean
square error between compensation signal and noise – disturbance. It can be written as:
2
( ) min ( ) ( )e n d n y n= − (1)
The quotients adaptation of impulse response for adaptive filter is described according to equa-
tion:
*
( 1) ( ) ( ) ( )w n w n e n x nμ+ = + ⋅ (2)
where μ – so-called adaptation step of the filter.
As usual the values of the adaptation step is less than unity and can be chosen within the range
0,3 -0,0001 to provide the proper algorithm convergence.
Implementation of the LMS algorithm directly without filtering of the reference signal cannot
provide the convergence due to delay in power amplifier and control loudspeaker. To acquire the
impulse response the white noise or swept sine test signal can be utilized. The S(z) as secondary
path response can be used for filtering of the reference signal further on as it shown in Fig. 1.
3.2 FxLMS algorithm for digital filtering – practical implementation using acoustic
feedback suppressor
To provide stability of the algorithm besides optimal choice of the convergence step μ the coun-
ter measures to suppress the acoustic feedback between control speaker and reference microphone
should be undertaken. To build up system in practice the software – hardware module using GDE
LabVIEW© and National Instruments© platform was chosen. It is allow to design active noise con-
trol system without writing specific programming code. The screen shot of the project for active
noise control system is depicted in Fig. 2.

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International Congress on Sound and Vibration
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Figure 2:Screen shots of the SW adaptive filter module in LabVIEW.
Adaptation step is 5·10-6
, filter size for primary path – 300 taps, Secondary path 110-taps, Filter
for suppression acoustic feedback reference microphone-loudspeaker – 110 – taps.
The project provided the acoustic feedback suppression between reference microphone and con-
trol loudspeaker and acquire data for feedback and secondary path using “white noise” test signal.
3.3 Experimental laboratory set-up, practical implementation
Figure 3:Sketch of experimental set-up for active noise control in the duct.

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International Congress on Sound and Vibration
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Figure 4:Embodiment of the active noise control system.
The Fig. 4 shows 1 – Duct with square cross-section 0.1 m by 0.1 m, 2 – Power amplifier LV
103, 3 – National Instruments board, 4 – measurement microphone, 5 – Control PC with SW Lab-
VIEW, 6 - Loudspeakers.
Figure 5:Measurement of the active noise control efficiency, the placement of the error and measurement
microphone at duct open end.
4. Discussion of the experimental results
To verify operation and noise reduction efficiency for active noise control in the channel the
short duct with length of 1 m and rectangular cross-section with size 0.2 m was used.
With this sizes of cross-section area the critical frequency for plane wave propagating in the
channel is f<(c/3d)<566 Hz. Here the с=340 m/s is a speed of sound and d=0.2 m. The electro dy-
namic loudspeaker with 10 watt electrical power and cone diameter of 9 cm was used as a noise

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source. The same loudspeaker was implemented to generate anti-noise as compensation one. The
enclosures for both loudspeakers were designed as cubic shaped boxes with same back volume of
10 liter. With given Thiele-Small parameters of loudspeakers the volume provides low frequency
cut-off around 100 Hz. To check active noise control efficiency the tonal and multi-tonal noise are
used to simulate the fan noise.
In Fig. 6 shown that total suppression of the global tone is equal to 50 dB and total level reduc-
tion in scale A is 30 dBА. We can conclude from obtained results that in real condition with well-
pronounced tonal components in fan noise or air conditioning system the reduction index using ac-
tive noise control technology would be high enough and audible. The suppression of the second
harmonic with frequency 720 Hz is negligible due to influence of the circumferential mode in the
duct for frequency higher than critical one (560 Hz).
20
30
40
50
60
70
80
90
100
110
120
0 100 200 300 400 500 600 700 800 900 1000f, Hz
L, dB
ANC ON (La=78,4 dBA)
ANC OFF (La=108,2 dBA)
Figure 6:Tonal signal (360 Hz) ANC on/ ANC off.
20
30
40
50
60
70
80
90
100
0 200 400 600 800 1000f, Hz
L, dB
ANC OFF (La=82,6 dBA)
ANC ON (La=70,1 dBA)
Figure 7:Multi tone experiment (100, 150, 200 Hz) ANC on/ ANC off.
During efficiency evaluation of the active noise control system using multi-tone noise source
(see. Fig. 5) total reduction index was 12 dBA, 6 dB at frequency 100 Hz and 20 dB for second and

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International Congress on Sound and Vibration
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third harmonics 200 Hz, 300 Hz respectively. The noise reduction at 100 Hz became lesser than for
second and third harmonics due to low efficiency of the loudspeaker at low frequency range deter-
mined by chosen back volume.
Conclusion
The practical prototype for active control of the fan noise in the duct comprises the reference and
error microphone power amplifier and control loudspeaker. Software and hardware embodiment of
FxLMS algorithm for adaptive signal filtering utilized the platform National Instruments and soft-
ware LabVIEW. The software module incorporates additional filter which provides acoustic feed-
back suppression between microphone and loudspeaker to increase the system stability and effi-
ciency. To implement system in real industrial condition the special measures to isolate micro-
phones from induced air flow noise and design cost-effective software/hardware module using sig-
nal processors Texas Instrumеnt or Analogue Devices.
REFERENCES
1 Lueg, P., Process of silencing sound oscillations, Patent US 2043416 A, (1936).
2 Nelson, P. A. and Elliott, S. J., Active Control of Sound, Academic Press, San Diego, (1992).
3 Kuo, S. M. and Morgan, D. R., Active noise control systems, John Wiley, New York, (1996).
4 Hansen, C. H., Understanding active noise cancellation, Spon Press, London and New York, (2001).
5 Snyder, S. D., Active Noise Control Primer, Springer Science+Business Media, New York, (2000).
6 Gasparov, М. S. and Kharitonov, М. Yu. Active noise control system based on measurement of particle
oscillatory velocity, Vestnik of Samara State Aerospace University, 1 (39), 107–114, (2013).