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Laboratory Duct Active noise control using Adaptive Filters

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Design and implement an Laboratory Duct noise cancellation system using an adaptive filter

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Laboratory Duct Active noise control using Adaptive Filters

  1. 1. NOISE CONTROL IN LABORATORY DUCT USING ADAPTIVE FILTER Submitted By: Rishikesh
  2. 2. GOAL: •The goal of the project is to design and implement an Laboratory Duct noise cancellation system using an adaptive filter.
  3. 3. LABORATORY DUCT BLOCK DIAGRAM
  4. 4. ACTIVE NOISE CONTROL • Active noise control (ANC) has received much attention in recent years. In an ANC system, a secondary source is introduced to generate anti-noise of equal amplitude but of opposite phase with reference to the primary noise. ANC techniques can be utilized to extract a signal buried in noise or to cancel unwanted noise.
  5. 5. WHAT IS NOISE ? • Noise means any unwanted sound. • Unwanted waveforms that can interfere with communication.
  6. 6. WHAT IS ACTIVE NOISE CANCELLATION ? • Method for reducing unwanted sound by the addition of a second sound specifically designed to cancel the first. WHAT IS ADAPTIVE FILTER ? • Adjust themselves to an ever-changing environment. • Changes its parameters so its performance improves through its surroundings. WHY WE USE ADAPTIVE FILTER? • Because some parameters of the desired processing operation are not known in advance or are changing.
  7. 7. ADAPTIVE FILTERS • A filter which adapts itself to the input signal given to it. • It is non-Linear and Time Variant. • The adaptive filtering system contains four signals: reference signal, d(n), input signal, x(n), output signal, y(n), and the error signal, e(n). The filter, w(n), adaptively adjusts its coefficients according to an optimization algorithm driven by the error signal. Σ N  y(n)  w n  x n  k k k 0 ( ) ( )
  8. 8. ADAPTIVE ALGORITHM • Least Mean Squares Algorithm (LMS) widely used Adaptive algorithm for noise cancellation. • The Least Mean Squares Algorithm (LMS) updates each coefficient on a sample-by-sample basis based on the error e(n). w (n 1) ( ) ( ) ( ) k w n e n x n k k     • μ (mu) is critical is Convergence Coefficient. • μ is set by trail and error for each Application.
  9. 9. APPROACHES OF ANC Feedforward Topology • Reference noise and cancelled noise are used • 2 inputs and 1 output Feedback Topology • Only cancelled noise are used – one input and one output
  10. 10. FEEDFORWARD TOPOLOGY Estimatio n of S(z), Ŝ(z) LMS - Secondary Path , S(z) x(n) x^(n) y(n) e(n) Duct system DSP System e(n) Primary function, P(z) y’(n) d(n) W(z) • Coherent input is captured, filtered and feed into LMS •Estimation of the secondary path transfer function is obtained by identification process -
  11. 11. FEEDFORWARD EXPERIMENTAL SETUP Noise speaker x(n) S(z) Input noise Canceling zone Canceling speaker Amplifier microphone Secondary Path e(n) y(n) NI PXIe - 1071
  12. 12. FUTURE PLAN: • Implement it on Hardware. • Use Laboratory Duct model technique to design Adaptive Active Noise Cancellation System. • Active noise cancellation with a fuzzy adaptive filtered-X algorithm.
  13. 13. APPLICATIONS • Noise Cancellation Headsets (headphone) • Bikes and cars. • Space satellite antennas. • Jet engines and heavy machinery. • Noise-Muter and more..
  14. 14. REFERENCES: • Adaptive recurrent fuzzy neural networks for active noise control (http://www.sciencedirect.com/science/article/pii/S0022460X06 002628) • Digital Signal Processing : Principles, Algorithms and Applications 4e by Proakis and Manolakis. • Signal Processing for Active Control by Stephen Elliott. • Wikipedia.org (http://en.wikipedia.org/wiki/Active_noise_control). • http://www.analog.com/library/analogDialogue/archives/34- 02/noise/ . • http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber= 6187929&queryText%3DActive+noise+control.
  15. 15. THANK YOU !

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