Active Noise Control
Architectures and Application
Potentials
Shawn Steenhagen - Applied Signal Processing, Inc.
3 Marsh Court
Madison, WI 53718
Tele: 608-441-9921
Fax: 608-441-9924
Web: www.appliedsignalprocessing.com
General Problem Definition:
• A measurable signal, y, contains a desired signal
component, d, and a noise component, n, which is to be
removed from y. The measurable signal can be a
humanly observable event such as a vibration or sound,
or an electrically observable event such as radio
frequency interference.
y=d+n
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The Active Noise Control Solution
• Feed Forward Adaptive Active Noise Control uses an LMS
Adaptive filter to create and introduce a control signal, ŷ which
when subtracted from y, results in an error signal whose power is
minimized in a mean square sense.
y=d+n
xref
ŷ

LMS Filter
-
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LMS Adaptive Filter
• Minimizes the error between the observation, y, and the
estimate, ŷ .
• Matches/Removes only the components y which are
correlated to the reference signal.
• Update EQ: A(k+1) = A(k) + mu*X(k)*e(k)
y=d+n
xref
ŷ

A
-
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An Active Noise Control System needs:
• A Reference Signal, x (either correlated to d or n).
• A measurable observation y or a measurable Error
Signal e= (y - ŷ )
• A method for adding/mixing the control signal ŷ into the
system. (either in the digital or physical domain)
y=d+n
xref
ŷ

LMS Filter
-
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ANC Feed Forward Architectures
• System or Plant Identification. (For Random and/or Tonal
noises)
• Signal Identification (For Tonal noises)
• “Filtered-X” variants of Plant Id or Signal Id Systems (for
acoustic and vibration control.)
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Filtered-X LMS
• Typical Adaptive Filter– measures the error signal
directly.
• Typical Acoustic Active Noise Control Configuration –
measures a filtered version (C) of the error signal.
• Requires use of the “Filtered-X” LMS algorithm.
• Update EQ: A(k+1) = A(k) + mu*{CX(k)}*eobs(k)
y = Px
xref

P
E
S
A
ŷ
C
e(k)obs = SEe(k)
C=SE
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ANC - Plant Identification Architecture
x(t)
y(t)
e(t)
P
E
^
-y’(t)
F
Acoustical
S
x(n)
e(t)
Signal Processing
0
e(n)
^
-y(n)
A
C
when
A
B
PE
SE
B
PF
^C
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^C
N
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Plant Id – Causality Requirements
A is causal if Pdelay > Sdelay
x(t)
y(t)
e(t)
P
E
^
-y’(t)
B is causal if PFdelay > 0
F
Acoustical
S
x(n)
e(t)
Signal Processing
0
e(n)
^
-y(n)
A
C
when
A
B
PE
SE
B
PF
^C
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^C
N
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ANC - Plant Identification Architecture
• Advantages:
– Can cancel random noise.
– Once converged, no need to re-adapt to track
changes in reference signal
• Disadvantages:
– Needs longer physical plant lengths to meet causality
requirements.
– Requires Persistence of Excitation for proper
convergence.
– Computationally intensive for higher filter order (# of
taps) when more frequency resolution is needed.
– Requires higher filter order for better low frequency
tonal performance.
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ANC – Signal Identification Architecture
y  y 0  y1  ...yn
Phase,
speed, or
rotational
info

E
-
yˆ  yˆ 0  yˆ1  ...yˆn
sin( 0 )
N
=
#
ers
d
r
o
D/A
ref[0]
A/D
As0
Tone
Generator

cos( 0 )

C
- 
ref[1]
Ac0
c_mu
galois
Noise
C
C
a_mu0
a_mu0
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ANC – Signal Identification Architecture
• Reference Signal Generator (from a phase or frequency
observation.)
• Two Tap Quadrature Adaptive Filter (One Pair for each frequency to
be controlled) matches phase and amplitude of frequency
component, yn , within y.
• Output and Update Equations:
yˆn (k )  Aˆ cosn (k )  cosn (k )  Aˆ sinn (k )  sin n (k )
Aˆ
(k  1)  Aˆ
(k )   cos (k )e(k )
cosn
cosn
n
Aˆ sinn (k  1)  Aˆ sinn (k )   sin n (k )e(k )
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ANC – Signal Identification Architecture
• Advantages:
– Fast Convergence.
– Computational Simplicity.
– Excellent frequency resolution.
• Disadvantages:
– Tonal or Periodic Noise Applications only.
– In Filtered-X LMS applications, requires 2N separate
filtering operations for each frequency component.
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Why is Acoustic Active Noise
Control Difficult?
• Typical Active Noise Control Configuration requires use
of the “Filtered-X” LMS algorithm.
• The C path must be known and typically it changes, so
an adaptive process for it is also required.
• The most reliable way to model the C path is using an
auxiliary noise source. This presents customer
acceptance challenges.
• Errors between C and SE effect convergence rates of
the adaptive filter and stability requirements.
• Complexity expands in MIMO cases. Number of C
models = NUM_ACT * NUM_ERR
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Crafting the ANC Solution:
• Evaluate Viability of Active as an Approach.
– Initial litmus tests – (physics & costs)
– Noise Analysis – characterize the noise.
– Market Analysis – cost & end user constraints.
• Choose Configuration:
– System ID, Signal ID or hybrid.
– Filtered-X vs. Direct LMS update.
• Simulation and Analysis
• Real Time Implementation.
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Viability Considerations of an ANC Solution
•
•
•
•
Availability of a reference signal, error signal, and mixing method.
Power Requirements.
Cost relative to Target Application.
Noise Spectrum Characteristics:
–
–
–
–
–
–
Tonal, random, or mix.
Dynamic or Stationary tonal characteristics.
SPL or Vibration Levels.
Frequency Range.
Geometric Attributes – plane wave, point source, free space.
Portion of which can be removed with active with respect to total noise
spectrum.
– Coherence between reference signal and observation or error signal.
•
•
•
•
Dimensionality of the system.
Size/geometry/packaging space.
Operating environment (hot, cold, caustic)
Complexity vs. Passive Methods. (Cost/Benefit)
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Potential ANC Applications
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Communication Systems (cell phone, two way radios,
intercom) within any noisy environment.**
Audio - Post Production clean up.
Aircraft – active engine mounts**
HVAC, Industrial Blowers/Fans**
Automotive – air induction*
Aircraft – cabin interior*
Vibration Isolation – sensitive manufacturing processes.*
Computer Fan Noise.*
Automotive - interior*, road noise.
Automotive – exhaust*,
Lawn mowers*, vacuum cleaners, dishwashers, refrigerators
Factory Noise in free space (hard to beat ear plugs)
Loud impulsive noise (jack hammers, punch presses)
Snoring, Neighbor or Teenager’s Stereo, Politicians.
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Easy/Practical
Hard/Impractical
Impossible
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Application Example
• Clean up Outbound Cell Tx in Vehicle During
Hands Free operation.
– Engine noise is tonal. (two tap quadrature can be
used)
– A reference signal can be generated from readily
available CAN signals.
– The mixing environment is in the digital domain Direct LMS can be applied.
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Application Example
Hands free echo cancellor
and engine noise cancellor
Gadc
sin(  0 )
Tone
Generator
A/D
ref[0]
D/A
A0

cos(  0 )
Gdac
ref[1]

A1

AEC
RPM
Calculation
a_mu0
a_mu1
CAN\
Converter
Cell Receive
Cell Transmit
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Conclusion/Looking Forward
• Increasing MIPS capacity of DSPs can make
computationally impractical applications of the
past more viable.
• Recent research in highly directional acoustic
sources via loud speaker arrays may help
expand potential application areas.
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References/Further Reading
• “Active Noise Control Systems – Algorithms and DSP
Implementations”; Sen M. Kuo, Dennis R. Morgan.
• “Lectures on Adaptive Parameter Estimation”, C. Richard
Johnson Jr.
• “Active Control of Sound”; P.A. Nelson & S.J. Elliot.
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