This paper presents a study overview of the Active Noise Cancellation (ANC) technology and demonstrates the technology with a real time setup. The paper highlights the innovation and challenges in demonstrating the technology. In the process the core Adaptive signal processing algorithm is explained in detail.

1. Active Noise Control Real Time Demonstration
Srikanth Konjeti
Srikanth.Konjeti@harman.com

2. Abstract: This paper presents a study
overview of the Active Noise Cancellation Noise
Error
(ANC) technology and demonstrates the Microphone
Microphone
technology with a real time setup. The paper
highlights the innovation and challenges in Noise
demonstrating the technology. In the process Source
the core Adaptive signal processing algorithm Anti noise
Loudspeaker
is explained in detail.
ANC
Controller
Keywords: Active Noise Control (ANC), Filtered –X
Least Mean Squares (FXLMS), Real time
Experiments, Secondary path Estimation “Fig.1. Active Noise Control Setup”
1. Introduction
Active Noise Control (ANC) is a technology that is used 2.1 Filtered-X LMS
in controlling the noise in the real life scenarios. The main e(n)
x(n) d(n)
objective is to generate anti noise which is out of phase
P(z) +
and equal in amplitude to the noise under test. The
technology uses the adaptive filter and adapts to the y(n) y’(n)
changes in the noise characteristics and generates anti
noise using the feed forward control mechanism. w(z) s(z)
ANC achieves noise reduction particularly at low
frequencies. The applications include Automotive, x’(n)
Appliances, Industrial and Transportation ^ LMS
s(z)
2. Overview “Fig.2. Block Diagram of FXLMS”
Acoustic noise and the noise related problems have been The block diagram of the FXLMS is shown in Fig.2 The
on the rise since the industrial revolution and the advent of primary noise x(n) passes through the primary path P(z)
machinery. The usual way to deal with noise reduction is and reaches the error microphone. The captured primary
to stuff the construction with bulky material which is noise x(n) is filtered with the adaptive filter and y(n) is
usually costly and is ineffective in reducing the low generated. The anti noise y(n) will be changed because of
frequencies. the secondary path between the loudspeaker and the error
The Active Noise control techniques deals with the low microphone. To compensate for the effects of the
frequencies effectively. The technique is primarily based secondary path the transfer function of the secondary path
on the superposition of the noise with the noise of equal is measured and placed in the path of the LMS algorithm.
amplitude and opposite phase resulting in a null at the
point of cancellation The accumulated input
The overview of the ANC technique is represented in 𝑿 𝒏 = [𝒙 𝒏 , 𝒙 𝒏 − 𝟏 , 𝒙 𝒏 − 𝟐 … 𝒙(𝒏 − 𝑳 − 𝟏)] 𝑻
the Fig.1. The Primary Noise is captured using a The accumulated Output
microphone or sensors and the anti noise is generated 𝒀 𝒏 = [𝒚 𝒏 , 𝒚 𝒏 − 𝟏 , 𝒚 𝒏 − 𝟐 … 𝒚(𝒏 − 𝑳 − 𝟏)] 𝑻
using the Anti Noise Loudspeaker. The resultant noise And the filter taps
after cancellation is captured by another microphone 𝑾 𝒏 = [𝒘 𝒏 , 𝒘 𝒏 − 𝟏 , 𝒘 𝒏 − 𝟐 … 𝒘(𝒏 − 𝑳 − 𝟏)] 𝑻
called the Error microphone (Fig.1). The error The secondary path
microphone acts as the feedback mechanism for the ANC 𝑺 𝒏 = [𝒔 𝒏 , 𝒔 𝒏 − 𝟏 , 𝒔 𝒏 − 𝟐 … 𝒔(𝒏 − 𝑳 − 𝟏)] 𝑻
controller. The filtered primary noise through the secondary path
The noise varies its amplitude, frequency with time and
𝑿′ 𝒏 = [𝒙′ 𝒏 , 𝒙′ 𝒏 − 𝟏 , 𝒙′ 𝒏 − 𝟐 … 𝒙′(𝒏 − 𝑳 − 𝟏)] 𝑻
the ANC keeps track of these changes and generates anti
noise using the adaptive filtering techniques.
The error between the primary noise and the anti noise
The anti noise loudspeaker is present in the path that the
with the secondary path
noise takes to reach the error microphone calling it a Feed
𝒆 𝒏 = 𝒅 𝒏 − 𝑺 𝑻 𝒏 ∗ 𝒀(𝒏)
forward cancelling technique. The Null zone is created at
The anti noise is generated from the adaptive filter
the error microphone.
The primary noise and the noise captured by the error 𝒚 𝒏 = 𝑾 𝑻 𝒏 𝑿(𝒏)
microphone is fed into the LMS based adaptive filter
The coefficients of the adaptive filter are continuously
which varies its filter coefficients to minimize the mean
adapted as the following
square error between the primary and the anti noise.
𝑾 𝒏 + 𝟏 = 𝑾 𝒏 + µ 𝒆 𝒏 𝑿(𝒏)
𝑿′ 𝒏 = 𝑺 𝒏 ∗ 𝑿 𝒏

3. 2.2 Real Time Experiment Setup
An impulse response is used in measuring the secondary
An experimental setup to demonstrate ANC is shown in path response. Fig.5, 6 shows the responses and noise at
the Fig.3. A pair of Harman Kardon HKTS speakers are low frequencies. This method of measurement in the noisy
used in the experiment. The speakers are connected to an environment is inefficient and destabilizes the LMS filter.
amplifier and the noise is played from the computer. One
speaker is used as the source of noise and the second
speaker is used to generate anti noise. A Behringer
microphone connected to the audio card is used as the
error microphone. The audio card is connected to the PC
via the USB. The DSP software runs on the PC as a VST
plug-in.
The noise is sent through the PC to a loudspeaker and
also to the VST plug-in. The signal captured by the error
microphone is also fed to the VST plug-in software. The
DSP software on the PC analyzes the noise source, the “Fig.5. Impulse Response of the Secondary Path”
error signal and generates the anti noise that is fed to the
second loudspeaker.
“Fig.6. Frequency Response of the
Secondary Path”
To overcome this problem we used sine waves with the
cancelling frequencies of interest as the source to measure
the transfer function. This is robust to the external noises
and accurately measures the transfer function at the
frequencies of interest. Fig.7 shows the secondary path
response measured with a sine wave of 200Hz.
“Fig.3. Error Microphone Setup”
Challenges
1. Measuring the Secondary path Response
2. Stability of the LMS algorithm
“Fig.7. Frequency Response of the
2.3 Secondary Path Transfer Function Secondary Path with 200Hz Sine wave”
The path from the Anti noise loudspeaker to the Error
Microphone is called the Secondary path.. As Shown in 2.4 Results
the Fig.4, white noise is played through the speaker and It is common in the industry and automobiles to find
the signal is captured through the error microphone. Both steady noise. It has audible discrete frequencies and of
the signals are fed to the LMS algorithm and over time the steady amplitude. So the sinusoids are used as noise here
filter converges to the transfer function of the secondary are played through the computer and connected to the
path. amplifier and to one of the speaker. The anti noise is
This method of measuring the transfer function is played from the computer to the loudspeaker. A switch is
effective when the measurement is taken over a very quiet placed in the VST plug-in (ANC OFF/ON) of the DSP
environment or the low frequency external noise will software to turn the algorithm OFF/ON. The Fig.8, 10
derail the response shows three plots
X(n) Anti Noise Error a) Sine wave as the noise input played through the
Speaker Microphone speaker.
White peaker
Secondary Path b) The Anti noise generated by the VST DSP software
Noise /
Impulse
and played through the anti noise speaker
y(n) c) The Error signal captured by the error microphone
The gaps in the anti noise plot shows the periods of
LMS ANC ON and OFF. It clearly establishes that when the
switch is OFF the error signal increases and when the
“Fig.4. Measure Secondary Path Transfer Function” switch is ON the error signal decreases.

4. The anti noise plot shows an initial period of 3-4
seconds where the adaptive filter ramps up (Adaptation
stage) to start cancelling the noise. In this period there is
no change in the error signal. Once the filer adapts to the
noise it remains steady and generates anti noise.
The convergence parameter of the LMS filter plays an
important role in the stability and performance of the
algorithm. The parameter is empirically tuned to have an
optimum performance and maintain stability.
The empirical value is shown in Table.1
“Fig.10. Multi tone Sine wave Input, Anti Noise, Error Signal”
“Fig.8. 200Hz Sine wave Input, Anti Noise, Error Signal”
The frequency response in Fig.9 shows the sine wave
when the ANC is OFF and ON. There is a reduction of
~50dB of the sine wave when the ANC is ON
“Fig.11. Frequency Response of the Multi tone Sine wave with
ANC OFF/ON”
The frequency plot shows a significant reduction in the
three frequencies when the ANC is switched ON.
3. Conclusion
The paper presents an insight into the ANC technology
and demonstrates the potential using a real time setup. The
technology has immense application in the automobile
industry and currently adopted in the automobiles to
suppress Engine noise and Road Noise. The real time
setup can be expanded to a real life application to create
“Fig.9. Frequency Response of Sine wave with ANC OFF/ON”
silent zones around the head of a person in the office and
Input ANC OFF ANC ON Reduction external environments.
Amplitude dB
Single Tone 20 -30 50 4. References
Multi Tone 10, 10, 10 -23, -10, -13 33, 20, 23 [1]. SEN M. KUO AND DENNIS R. MORGAN, “Active
Convergence 0.0002
µ
Noise Control: A Tutorial Review”.
“Table.1. Performance of ANC”
[2]. Lichuan Liu, Sen M. Kuo, and Kishan P. Raghuathan.
“Active Noise Control for Motorcycle Helmet”.
Multiple Sinusoids
The ANC experiment is carried on multiple sine waves of
150+200+250 Hz which sounds like the motorbike noise
on the road.