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A Study on Noise Reduction in an Enclosed
Space by Destructive Interference
By
Lt Col (Dr) RM Monaragala
Maj KVP Dhammika
Flt Lt EPDR Edirisinghe
Content
• Background
• Aim
• ANC Principles
• Literature Review
• Practical Experiment and Results
• Future Applications
• Conclusion
• Questions
Background
Research on noise control has become
important
Exposure to the noise may result in deafness
and cardiovascular complaints.
Acute effects of noise include changes in
performance efficiency.
Noise Sources
Air Condition fan, aircraft propeller noises fall
below 500 Hz
Interior Noises
• Propeller Noise: Inside an aircraft.
– Single low frequency, around 200 to 400 Hz.
• Engine Noise: Inside an automobile.
– Single low frequency , around 200 to 300 Hz.
• Road Noise: Inside an automobile.
– Wide band low frequency noise from tires on road
– Range from 100 Hz to 500 Hz.
Interior Noises
• Aerodynamic Noise: Wind noise
– High frequency noise above 1000 Hz.
– Caused by wind sweeping through partly openings to the vehicle
interior
• Air Condition fan Noise
– Passes through air condition ducts to an enclosed space
• Structure borne vibrations
– Low frequency noise.
Active Noise Control (ANC)
Primary Noise Waveform Anti Noise Waveform
Residual Noise
Aim
To develop Active Noise Controller to reduce
low frequency noise and use for the defence
applications.
Importance of Reducing Interior Noise Level
• Minimize the fatigue on driver & passengers in
an automobile
• Improve the audio environment (for music)
• Improve the comfort of passengers
• Make the class rooms Quiet
ANC Principles
Noise
• Broadband Noise
- Ex: Low-frequency sound of a jet plane, the
impulse noise of a gunshot.
• Narrowband Noise
- Ex: The noise caused by a car’s combustion
engine.
Noise Reduction Methods
• Active Method
– Effective to reduce Noise up to 500 Hz.
• Passive Method
–Effective to reduce Noise beyond 500 Hz.
Passive Methods
• Sound absorption
• Sound isolation
• Vibration isolation
• Vibration damping
• Mufflers
An ANC system can be effective across the
entire noise spectrum, but it is particularly
appropriate at low frequencies of up to 500 Hz,
where passive systems are less effective.
Destructive Interference
ANC is developing rapidly due to improvements
in noise control, often with potential benefits
in size, weight, volume, and cost.
Different Kinds of ANC
ANC systems are divided into three different
groups.
• Global free space cancellation
• Zone-of-silence cancellation
• Cavity and duct cancellation
Feed Back ANC System
Feed Forward ANC System
Narrowband Feed Forward ANC System
Destructive Interference
The resulting sound is null, the sound energy is
transformed into heat
The net result is no sound at all.
Literature Review
A microphone & a loudspeaker to generate a
canceling sound was first proposed & patented
by Lueg in 1936.
Burgess developed a duct-noise cancellation
system based on adaptive filter theory in 1981.
Later in the 1980s, Digital Signal Processing was
Developed
The specialized DSPs were designed for real-
time numerical processing of digitized signals.
Adaptive Filters – LMS Algorithm
Adaptive Filters
• The principle is to calculate an output that is
equal to the unwanted disturbance by
minimizing the error signal.
• The disturbance will then be cancelled by
superposition of the inverted output of the
filter on it.
ANC Controller
• The ANC Controller is a Finite Impulse Response Filter.
• It computes the anti phase audio signal using a Least
Mean Square Error algorithm.
LMS Algorithm
• Output Signal Calculated as
• Primary Signal (input to ANC)
• Weight vector at time n
 )1(..)1()()(  Nnxnxnxnx
 T
N
T
nwnwnwnw )(..)()()( 110 




1
0
)()()(
N
i
i inxnwny
LMS Algorithm
• Then the output signal y(n) in equation can be
expressed by the vector operation
• The error (difference in phase between the
primary and secondary) can be written as
)()()()( ndnxnwne T

)()()()()(
1
0
inxnwnxnwny
N
i
i
T
 


Optimization
• Objective of the LMS algorithm is to find the
optimal weight vector where, the mean square
value of the error signal e(n) is minimized.
• This results in the weight vector to be updated
as:
)().()()1( nenxnwnw 
)()( nxnxT

 
20 
Practical Experiment and Results
ANC Simulation Model
The concept was simulated using Labview based on
the following model:
Initial ANC Experimental Model With
Labview
ANC Simulation With Labview
Wide band Noise
Spectrum without ANC
Wideband Noise Spectrum
with ANC
PSD of Noise at Error
Microphone Without Control
PSD of Noise at Error
Microphone with Control using
the LMS Algorithm
Initial Test Results Obtained With JAVA
Experimental Model
Initial Test Results Obtained With JAVA
Experimental Model
Future Applications
ANC Application In Defence
• Aid to reduce aircraft interior noise
• Aid to reduce engine vibration noise in cabins
of ships
• Reduce engine boom in a tank interior
• Reduce the Air-condition fan noise inside the
Classroom
Future Plans
• Develop the same model to obtain more
filtered effect.
• Develop the circuitry for the use of opened
space to reduce environmental noise.
• Develop the circuitry for the use of airfield to
reduce jet aircraft engine noise.
Conclusion
• The different control methods available today
for active noise control were studied.
• The adaptive filters based on the LMS algorithm
were chosen.
• Labview model and JAVA model were developed
to test the ANC level & to obtain graphical views
of controlled signal.
• The model will be developed in future for the
defence applications.
• ?
Interior Noise Spectrum
Passive Techniques
– Sound Absorption
Sound Absorbing
Material
RigidWall
Reflected Sound
Pressure (Pr)
Incident Sound
Pressure (Pi)
2
1
r
i
P
P
NAC 
Noise Absorption
Coefficient
Back
Time Series Data of Input Noise Without Control
Time Series Data of Noise at Error Microphone With
an Adaptive Controller Using a Step
Size of 0.01 for the LMS Algorithm
Time Series Data of Noise at Error Microphone With
an Adaptive Controller Using a Step
Size of 0.1 for the LMS Algorithm
Developed ANC Laboratory Experimental
Model
REFERENCE
MICROPHONE
PRIMARY SOUND
SOURCE
ERROR
MICROPHONE
POWER AMPLIFIER
#2
POWER AMPLIFIER
#1
PRE AMPLIFIER #1
PRE AMPLIFIER #2 M6259 DATA ACQUISITION
CARD
PC
ENCLOSURE

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Symposium Presentation

  • 1. A Study on Noise Reduction in an Enclosed Space by Destructive Interference By Lt Col (Dr) RM Monaragala Maj KVP Dhammika Flt Lt EPDR Edirisinghe
  • 2. Content • Background • Aim • ANC Principles • Literature Review • Practical Experiment and Results • Future Applications • Conclusion • Questions
  • 4. Research on noise control has become important
  • 5. Exposure to the noise may result in deafness and cardiovascular complaints.
  • 6. Acute effects of noise include changes in performance efficiency.
  • 8. Air Condition fan, aircraft propeller noises fall below 500 Hz
  • 9. Interior Noises • Propeller Noise: Inside an aircraft. – Single low frequency, around 200 to 400 Hz. • Engine Noise: Inside an automobile. – Single low frequency , around 200 to 300 Hz. • Road Noise: Inside an automobile. – Wide band low frequency noise from tires on road – Range from 100 Hz to 500 Hz.
  • 10. Interior Noises • Aerodynamic Noise: Wind noise – High frequency noise above 1000 Hz. – Caused by wind sweeping through partly openings to the vehicle interior • Air Condition fan Noise – Passes through air condition ducts to an enclosed space • Structure borne vibrations – Low frequency noise.
  • 11. Active Noise Control (ANC) Primary Noise Waveform Anti Noise Waveform Residual Noise
  • 12. Aim To develop Active Noise Controller to reduce low frequency noise and use for the defence applications.
  • 13. Importance of Reducing Interior Noise Level • Minimize the fatigue on driver & passengers in an automobile • Improve the audio environment (for music) • Improve the comfort of passengers • Make the class rooms Quiet
  • 15. Noise • Broadband Noise - Ex: Low-frequency sound of a jet plane, the impulse noise of a gunshot. • Narrowband Noise - Ex: The noise caused by a car’s combustion engine.
  • 16. Noise Reduction Methods • Active Method – Effective to reduce Noise up to 500 Hz. • Passive Method –Effective to reduce Noise beyond 500 Hz.
  • 17. Passive Methods • Sound absorption • Sound isolation • Vibration isolation • Vibration damping • Mufflers
  • 18. An ANC system can be effective across the entire noise spectrum, but it is particularly appropriate at low frequencies of up to 500 Hz, where passive systems are less effective.
  • 20. ANC is developing rapidly due to improvements in noise control, often with potential benefits in size, weight, volume, and cost.
  • 21. Different Kinds of ANC ANC systems are divided into three different groups. • Global free space cancellation • Zone-of-silence cancellation • Cavity and duct cancellation
  • 22. Feed Back ANC System
  • 26. The resulting sound is null, the sound energy is transformed into heat
  • 27. The net result is no sound at all.
  • 29. A microphone & a loudspeaker to generate a canceling sound was first proposed & patented by Lueg in 1936.
  • 30. Burgess developed a duct-noise cancellation system based on adaptive filter theory in 1981.
  • 31. Later in the 1980s, Digital Signal Processing was Developed
  • 32. The specialized DSPs were designed for real- time numerical processing of digitized signals.
  • 33. Adaptive Filters – LMS Algorithm
  • 34. Adaptive Filters • The principle is to calculate an output that is equal to the unwanted disturbance by minimizing the error signal. • The disturbance will then be cancelled by superposition of the inverted output of the filter on it.
  • 35. ANC Controller • The ANC Controller is a Finite Impulse Response Filter. • It computes the anti phase audio signal using a Least Mean Square Error algorithm.
  • 36. LMS Algorithm • Output Signal Calculated as • Primary Signal (input to ANC) • Weight vector at time n  )1(..)1()()(  Nnxnxnxnx  T N T nwnwnwnw )(..)()()( 110      1 0 )()()( N i i inxnwny
  • 37. LMS Algorithm • Then the output signal y(n) in equation can be expressed by the vector operation • The error (difference in phase between the primary and secondary) can be written as )()()()( ndnxnwne T  )()()()()( 1 0 inxnwnxnwny N i i T    
  • 38. Optimization • Objective of the LMS algorithm is to find the optimal weight vector where, the mean square value of the error signal e(n) is minimized. • This results in the weight vector to be updated as: )().()()1( nenxnwnw  )()( nxnxT    20 
  • 40. ANC Simulation Model The concept was simulated using Labview based on the following model:
  • 41. Initial ANC Experimental Model With Labview
  • 42. ANC Simulation With Labview Wide band Noise Spectrum without ANC Wideband Noise Spectrum with ANC
  • 43. PSD of Noise at Error Microphone Without Control PSD of Noise at Error Microphone with Control using the LMS Algorithm
  • 44. Initial Test Results Obtained With JAVA Experimental Model
  • 45. Initial Test Results Obtained With JAVA Experimental Model
  • 47. ANC Application In Defence • Aid to reduce aircraft interior noise • Aid to reduce engine vibration noise in cabins of ships • Reduce engine boom in a tank interior • Reduce the Air-condition fan noise inside the Classroom
  • 48. Future Plans • Develop the same model to obtain more filtered effect. • Develop the circuitry for the use of opened space to reduce environmental noise. • Develop the circuitry for the use of airfield to reduce jet aircraft engine noise.
  • 49. Conclusion • The different control methods available today for active noise control were studied. • The adaptive filters based on the LMS algorithm were chosen. • Labview model and JAVA model were developed to test the ANC level & to obtain graphical views of controlled signal. • The model will be developed in future for the defence applications.
  • 50. • ?
  • 51.
  • 52.
  • 53.
  • 55. Passive Techniques – Sound Absorption Sound Absorbing Material RigidWall Reflected Sound Pressure (Pr) Incident Sound Pressure (Pi) 2 1 r i P P NAC  Noise Absorption Coefficient Back
  • 56. Time Series Data of Input Noise Without Control
  • 57. Time Series Data of Noise at Error Microphone With an Adaptive Controller Using a Step Size of 0.01 for the LMS Algorithm
  • 58. Time Series Data of Noise at Error Microphone With an Adaptive Controller Using a Step Size of 0.1 for the LMS Algorithm
  • 59. Developed ANC Laboratory Experimental Model REFERENCE MICROPHONE PRIMARY SOUND SOURCE ERROR MICROPHONE POWER AMPLIFIER #2 POWER AMPLIFIER #1 PRE AMPLIFIER #1 PRE AMPLIFIER #2 M6259 DATA ACQUISITION CARD PC ENCLOSURE