Active Noise Control Real Time Demonstration

Srikanth Konjeti Srikanth.Konjeti@harman.com

Abstract: 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.

Keywords: Active Noise Control (ANC), Filtered –X

Least Mean Squares (FXLMS), Real time

Experiments, Secondary path Estimation

1. Introduction

Active Noise Control (ANC) is a technology that is used

in controlling the noise in the real life scenarios. The main

objective is to generate anti noise which is out of phase

and equal in amplitude to the noise under test. The

technology uses the adaptive filter and adapts to the

changes in the noise characteristics and generates anti

noise using the feed forward control mechanism.

ANC achieves noise reduction particularly at low

frequencies. The applications include Automotive,

Appliances, Industrial and Transportation

2. Overview

Acoustic noise and the noise related problems have been

on the rise since the industrial revolution and the advent of

machinery. The usual way to deal with noise reduction is

to stuff the construction with bulky material which is

usually costly and is ineffective in reducing the low

frequencies.

The Active Noise control techniques deals with the low

frequencies effectively. The technique is primarily based

on the superposition of the noise with the noise of equal

amplitude and opposite phase resulting in a null at the

point of cancellation

The overview of the ANC technique is represented in

the Fig.1. The Primary Noise is captured using a

microphone or sensors and the anti noise is generated

using the Anti Noise Loudspeaker. The resultant noise

after cancellation is captured by another microphone

called the Error microphone (Fig.1). The error

microphone acts as the feedback mechanism for the ANC

controller.

The noise varies its amplitude, frequency with time and

the ANC keeps track of these changes and generates anti

noise using the adaptive filtering techniques.

The anti noise loudspeaker is present in the path that the

noise takes to reach the error microphone calling it a Feed

forward cancelling technique. The Null zone is created at

the error microphone.

The primary noise and the noise captured by the error

microphone is fed into the LMS based adaptive filter

which varies its filter coefficients to minimize the mean

square error between the primary and the anti noise.

“Fig.1. Active Noise Control Setup”

2.1 Filtered-X LMS

“Fig.2. Block Diagram of FXLMS”

The block diagram of the FXLMS is shown in Fig.2 The

primary noise x(n) passes through the primary path P(z)

and reaches the error microphone. The captured primary

noise x(n) is filtered with the adaptive filter and y(n) is

generated. The anti noise y(n) will be changed because of

the secondary path between the loudspeaker and the error

microphone. To compensate for the effects of the

secondary path the transfer function of the secondary path

is measured and placed in the path of the LMS algorithm.

The accumulated input

𝑿 𝒏 = [𝒙 𝒏 , 𝒙 𝒏 − 𝟏 , 𝒙 𝒏 − 𝟐 …𝒙(𝒏 − 𝑳 − 𝟏)]𝑻 The accumulated Output

𝒀 𝒏 = [𝒚 𝒏 , 𝒚 𝒏 − 𝟏 , 𝒚 𝒏 − 𝟐 …𝒚(𝒏 − 𝑳 − 𝟏)]𝑻

And the filter taps

𝑾 𝒏 = [𝒘 𝒏 , 𝒘 𝒏 − 𝟏 , 𝒘 𝒏 − 𝟐 …𝒘(𝒏 − 𝑳 − 𝟏)]𝑻

The secondary path

𝑺 𝒏 = [𝒔 𝒏 , 𝒔 𝒏 − 𝟏 , 𝒔 𝒏 − 𝟐 …𝒔(𝒏 − 𝑳 − 𝟏)]𝑻

The filtered primary noise through the secondary path

𝑿′ 𝒏 = [𝒙′ 𝒏 , 𝒙′ 𝒏 − 𝟏 , 𝒙′ 𝒏 − 𝟐 …𝒙′(𝒏 − 𝑳 − 𝟏)]𝑻

The error between the primary noise and the anti noise

with the secondary path

𝒆 𝒏 = 𝒅 𝒏 − 𝑺𝑻 𝒏 ∗ 𝒀(𝒏)

The anti noise is generated from the adaptive filter

𝒚 𝒏 = 𝑾𝑻 𝒏 𝑿(𝒏)

The coefficients of the adaptive filter are continuously

adapted as the following

𝑾 𝒏 + 𝟏 = 𝑾 𝒏 + µ 𝒆 𝒏 𝑿(𝒏)

𝑿′ 𝒏 = 𝑺 𝒏 ∗ 𝑿 𝒏

Anti noise

Loudspeaker

Noise

Source

ANC

Controller

Error

Microphone

Noise

Microphone

^

s(z)

w(z) s(z)

P(z)

LMS

+

e(n)

y(n) y’(n)

x(n) d(n)

x’(n)

2.2 Real Time Experiment Setup

An experimental setup to demonstrate ANC is shown in

the Fig.3. A pair of Harman Kardon HKTS speakers are

used in the experiment. The speakers are connected to an

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

error signal and generates the anti noise that is fed to the

second loudspeaker.

“Fig.3. Error Microphone Setup”

Challenges

1. Measuring the Secondary path Response

2. Stability of the LMS algorithm

2.3 Secondary Path Transfer Function

The path from the Anti noise loudspeaker to the Error

Microphone is called the Secondary path.. As Shown in

the Fig.4, white noise is played through the speaker and

the signal is captured through the error microphone. Both

the signals are fed to the LMS algorithm and over time the

filter converges to the transfer function of the secondary

path.

This method of measuring the transfer function is

effective when the measurement is taken over a very quiet

environment or the low frequency external noise will

derail the response

“Fig.4. Measure Secondary Path Transfer Function”

An impulse response is used in measuring the secondary

path response. Fig.5, 6 shows the responses and noise at

low frequencies. This method of measurement in the noisy

environment is inefficient and destabilizes the LMS filter.

“Fig.5. Impulse Response of the Secondary Path”

“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.7. Frequency Response of the

Secondary Path with 200Hz Sine wave”

2.4 Results

It is common in the industry and automobiles to find

steady noise. It has audible discrete frequencies and of

steady amplitude. So the sinusoids are used as noise here

are played through the computer and connected to the

amplifier and to one of the speaker. The anti noise is

played from the computer to the loudspeaker. A switch is

placed in the VST plug-in (ANC OFF/ON) of the DSP

software to turn the algorithm OFF/ON. The Fig.8, 10

shows three plots

a) Sine wave as the noise input played through the

speaker.

b) The Anti noise generated by the VST DSP software

and played through the anti noise speaker

c) The Error signal captured by the error microphone

The gaps in the anti noise plot shows the periods of

ANC ON and OFF. It clearly establishes that when the

switch is OFF the error signal increases and when the

switch is ON the error signal decreases.

y(n)

LMS

White

Noise /

Impulse

Anti Noise Speaker

Error Microphone

peaker

X(n)

Secondary Path

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.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.9. Frequency Response of Sine wave with ANC OFF/ON”

Input ANC OFF ANC ON Reduction

Amplitude dB

Single Tone 20 -30 50

Multi Tone 10, 10, 10 -23, -10, -13 33, 20, 23

Convergence

µ

0.0002

“Table.1. Performance of ANC”

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.

“Fig.10. Multi tone Sine wave Input, Anti Noise, Error Signal”

“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

silent zones around the head of a person in the office and

external environments.

4. References

[1]. SEN M. KUO AND DENNIS R. MORGAN, “Active

Noise Control: A Tutorial Review”.

[2]. Lichuan Liu, Sen M. Kuo, and Kishan P. Raghuathan.

“Active Noise Control for Motorcycle Helmet”.