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Adaptive Active Control of Sound in Smart Rooms (2014)

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Adaptive Active Control of Sound in Smart Rooms (2014)

  1. 1. ADAPTIVE ACTIVE CONTROL OF SOUND IN SMART ROOMS DR. IMAN ARDEKANI
  2. 2. This Presentation Introduction2 Stabilization of ANC in Smart Rooms3 1 Background Remote Acoustic Sensing in Smart Rooms4 Experiments5 Conclusion and Future Work6 2
  3. 3. Background Why ANC in Smart rooms: ① Evolving technological approaches ② Future of living rooms, hospital rooms office rooms & class rooms ③ Research with impact Smart Rooms 3
  4. 4. Background ① Interesting theory in Physics ② Interesting theory in Control Engineering ③ High-tech design & implementation, e.g. FPGA, DSP ④ New challenges (when integrated with smart rooms) Active Noise Control 4
  5. 5. Background Active Noise Control / Smart Sensor Networks for Passenger Cars Professional Research on ANC and Smart Rooms - Founder of Unitec Smart Rooms - Adaptive ANC in Smart Rooms - Finalist of IBM Innovation Award 2014 PhD & FRDF Postdoctoral Research on Adaptive ANC - More than 30 journal/conference papers on ANC - International research collaboration - Nominated for UoA Top Doctoral Thesis Award (by FoE) 5
  6. 6. INTRODUCTION Smart Rooms Adaptive ANC Challenges 6
  7. 7. Enhancing Quality of Life in Smart Living Rooms 7
  8. 8. Enhancing Human Productivity in Smart Office Rooms 8
  9. 9. Enhancing Quality of Care in Smart Hospital Rooms 9
  10. 10. Enhancing Quality of Learning in Smart Class Rooms 10
  11. 11. AdaptiveANCinSmartRooms–ByImanArdekani Smart Rooms’ Structure - Proposed Acoustic Sig Pro Medical Sig Pro Features Room BookRoom Book Features Intelligent Agents Robots Room Book Room Book 11
  12. 12. AdaptiveANCinSmartRooms–ByImanArdekani This Seminar Focus Acoustic Sig Pro Medical Sig Pro Room BookRoom Book Intelligent Agents Robots Room Book Room Book Features 12
  13. 13. AdaptiveANCinSmartRooms–ByImanArdekani Why ANC in Smart Rooms? Acoustic Sig Pro Intelligent Agents Noise Why Noise Control? 1. Human productivity (especially in open plan office rooms) 2. Quality of life (more pleasant rooms) 3. Quality of interaction between smart room and occupants (speech) Why not Passive Noise Control? • Passive Noise Control is bulky and inefficient for low frequency. 13
  14. 14. AdaptiveANCinSmartRooms–ByImanArdekani ANC in Smart Rooms Acoustic Sig Pro Intelligent Agents Noise Residual Noise Noise Anti-Noise 1) can not be measured. 2) and are unknown dynamic systems. P S P S 14
  15. 15. AdaptiveANCinSmartRooms–ByImanArdekani ANC Stability 1 2 3 Uncertainty Stability Low Moderate High 1 2 3 Traditional ANC algorithms suffer low robustness in real-life applications. 15
  16. 16. AdaptiveANCinSmartRooms–ByImanArdekani Acoustic Sensing in ZoQ - It is essential to place an Error Microphone (Err Mic.) in the desired quiet zone to sense the residual noise. - The size of the quiet zone is very limited. - Can we use one of the smart room’s built-in microphone instead? - If we can, then - 1) more effective use of space in the quiet zone - 2) more cost effective hardware design 10 dB ZoQ Noise P S 𝜆 20 Err Mic 17
  17. 17. STABILIZATION OF ANC IN SMART ROOMS Root Locus (RL) Method ANC RL Plots Stabilization Process 18
  18. 18. AdaptiveANCinSmartRooms–ByImanArdekani Numerical methods Root Locus Method Char Eq. 𝐵 𝑧 = 0 LTI Digital Systems Numerical methods x x x z-plane 𝐺(𝑧) 𝐺(𝑧) Char Eq. 𝐵(𝑧) + 𝑘𝐴(𝑧) = 0 RL 𝑘 = 0 ✗ 𝐺 𝑧 = 𝑎0 + 𝑎1 𝑧 + 𝑎2 𝑧2 + ⋯ 𝑏0 + 𝑏1 𝑧 + 𝑏2 𝑧2 + ⋯ 𝐺 𝑧 = 𝐴(𝑧) 𝐵(𝑧) 𝐻 𝑧 = 𝐺(𝑧) 1 + 𝑘𝐺(𝑧) = 𝐴(𝑧) 𝐵(𝑧) + 𝑘𝐴(𝑧) 𝑘 𝑘 = ∞ x x z-plane x x 19
  19. 19. AdaptiveANCinSmartRooms–ByImanArdekani 2 1 Root Locus Methods LTI Digital Systems Char Eq. 𝐵(𝑧) + 𝑘𝐴(𝑧) = 0 RL 𝐺(𝑧) 𝐺(𝑧) 𝑘 Char Eq. 𝐵 𝑧 = 0 Numerical methods x x x z-plane 𝐺 𝑧 = 𝑎0 + 𝑎1 𝑧 + 𝑎2 𝑧2 + ⋯ 𝑏0 + 𝑏1 𝑧 + 𝑏2 𝑧2 + ⋯ 𝐺 𝑧 = 𝐴(𝑧) 𝐵(𝑧) 𝐻 𝑧 = 𝐺(𝑧) 1 + 𝑘𝐺(𝑧) = 𝐴(𝑧) 𝐵(𝑧) + 𝑘𝐴(𝑧) x z-plane x x𝑘 = 0 𝑘 = ∞ x 20
  20. 20. AdaptiveANCinSmartRooms–ByImanArdekani Root Locus Method 1 LTI Digital Systems 2 Char Eq. 𝐵(𝑧) + 𝑘𝐴(𝑧) = 0 21
  21. 21. AdaptiveANCinSmartRooms–ByImanArdekani 𝐴(𝑧) ANC RL Plots • Adaptation process performed by the FxLMS in adaptive ANC is a recursive and non-linear process. • However, by using the Independence Assumptions, a linear approximation for the FxLMS adaptation process can be obtained. • FxLMS stability is highly related to the adaptation step-size k so a stability analysis w.r.t. k is needed. 𝑧 𝑄 − 𝑧 𝑄−1 + 𝑘 𝑃𝑥 𝑚=𝑞 𝑄−1 𝑠 𝑚 2 𝑧 𝑄−1−𝑚 = 0 𝐵(𝑧) 2 Char Eq. 𝐵(𝑧) + 𝑘𝐴(𝑧) = 0 1 LTI Digital Systems 𝑘 is an available parameter but 𝐴(𝑧) and 𝐵(𝑧) do not physically exist . 𝑞 𝑄 − 1 Room impulse response Amplitude Time index 𝑠 𝑞 𝑠 𝑄−1 noise power. 22
  22. 22. AdaptiveANCinSmartRooms–ByImanArdekani ANC RL Plots 𝐴(𝑧) coefficients are always positive . 𝐴(𝑧) 𝑧 𝑄 − 𝑧 𝑄−1 + 𝑘 𝑃𝑥 𝑚=𝑞 𝑄−1 𝑠 𝑚 2 𝑧 𝑄−1−𝑚 = 0 𝐵(𝑧) 𝐵 𝑧 = 𝑧 𝑄 − 𝑧 𝑄−1 Typical Trajectories for the FxLMS Root Locus (independent of the room acoustics) 23
  23. 23. AdaptiveANCinSmartRooms–ByImanArdekani ANC RL Plots z-plane Start points of ANC RL are located at the roots of B(z). B(z)=zQ-z Q-1 Q-1 start points at the origin and 1 start point at z=1. z=0 z=1 24
  24. 24. AdaptiveANCinSmartRooms–ByImanArdekani ANC RL Plots z-plane The real interval of (0,1) belongs to the ANC RL. 25
  25. 25. AdaptiveANCinSmartRooms–ByImanArdekani ANC RL Plots z-plane 𝑥 𝐵 B1 B2 There is always a breakaway point on (0,1), given by 𝑥 𝐵 = 𝐷 𝑒𝑞 𝐷 𝑒𝑞+1 . (close to z=1) 26
  26. 26. AdaptiveANCinSmartRooms–ByImanArdekani ANC RL Plots z-plane 𝑥 𝐵 B1 B2 B4 B3 There are still Q-2 start points at the origin: B3, B4 … BQ starts from the origin, moving towards their end points (can take any shapes). 27
  27. 27. AdaptiveANCinSmartRooms–ByImanArdekani ANC RL Plots z-plane 𝑥 𝐵 B1 B2 B4 B3 o o to infinite end points to infinite end points Finite End Points at roots of A(z). A(z) coefficients: sq 2: positive so all the finite end points are in the left side of Imaginary axis. 28
  28. 28. AdaptiveANCinSmartRooms–ByImanArdekani ANC RL Plots z-plane 𝑥 𝐵 B4 B3 o o increasing 𝑘 k=0 B1 B2 29
  29. 29. AdaptiveANCinSmartRooms–ByImanArdekani Stabilization Process z-plane 𝑥 𝐵 B1 B2 B4 B3 o o The root moving on B1 - is closest root to the critical point z=1. - is dominant! 30
  30. 30. AdaptiveANCinSmartRooms–ByImanArdekani Stabilization Process z-plane 𝑥 𝐵 B1 B2 B4 B3 o o The short distance of this root from z=1 restricts the stability margin of adaptive ANC. 31
  31. 31. AdaptiveANCinSmartRooms–ByImanArdekani Stabilization Process z-plane 𝑥 𝐵 B1 B2 B4 B3 o o Can we detour B1 so that it can go further from the critical point ? 32
  32. 32. AdaptiveANCinSmartRooms–ByImanArdekani Stabilization Process z-plane 𝑥 𝐵 B1 B2 B4 B3 o o How? By introducing a RL end point in (0,1): a new root for A(z): A(z)⟶(z-ξ)A(z) 33
  33. 33. AdaptiveANCinSmartRooms–ByImanArdekani Stabilization Process A(z)⟶(z-ξ)A(z) z-plane B1 o 𝑥 𝐵 B2 B4 B5 o o B3 𝝃 A(z)⟶(z-ξ)A(z) 34
  34. 34. AdaptiveANCinSmartRooms–ByImanArdekani Stabilization Process Modified RLOriginal RL 𝑥 𝐵 B1 B2 B4 B5 o o B1 o B2 B4 B3 o o B3 𝝃 35
  35. 35. AdaptiveANCinSmartRooms–ByImanArdekani Stabilization Process • 𝐴(𝑧) ⟶ (𝑧 − 𝜉)𝐴(𝑧) • but 𝐴(𝑧) is not an actual systems. Room Impulse Response x Ref Mic. Err Mic. + Current Filter Parameters Updated Filter Parameters C(z) Compensator 𝐶 𝑧 = 1 − 𝜉 1 − 𝜉𝑧−1 36
  36. 36. STABILIZATION OF ANC IN SMART ROOMS Summary • A novel method based on RL theory is developed. • RL analysis of the adaptation process leads to develop new adaptive ANC algorithms. • The new algorithms show good stability behavior in smart rooms. • It is proved mathematically. • Simulation and practical results support the theoretical findings. 37
  37. 37. REMOTE ACOUSTIC SENSING IN SMART ROOMS ANC Analysis in Acoustic Domain Remote ANC Systems 38
  38. 38. AdaptiveANCinSmartRooms–ByImanArdekani Error Microphone Location 𝜆 20 Traditional ANC Problems: - very small zone of quiet - space occupied by the error mic 10 dB ZoQ 10 dB ZoQ Advantage: - effective use of space in quiet zones - Effective use of smart room hardware resources Remote ANC (proposed) e(n) e(n) 39
  39. 39. AdaptiveANCinSmartRooms–ByImanArdekani ANC Analysis in Acoustic Domain 𝑊𝑜𝑝𝑡 𝑊𝑜𝑝𝑡 𝑧 = 𝑈 𝑥(𝑧) 𝑈 𝑦(𝑧) 𝐾𝑥𝑦 𝑧−∆ 𝑥𝑦 x(n) Ref Mic Lx y(n) Control Source Ly Lx Ly Lo 𝑑 𝑦 𝑑 𝑥 𝜑 𝑦Lr 𝑑 𝑟 e(n) Lo Err Mic (ZoQ) 40
  40. 40. AdaptiveANCinSmartRooms–ByImanArdekani ANC Analysis in Acoustic Domain • Since 𝑈 𝑥(𝑧) and are 𝑈 𝑦(𝑧) unknown, we cannot implement 𝑊𝑜𝑝𝑡 𝑧 directly but The FxLMS algorithm can adaptively adjust the adaptive filter to an estimate of 𝑊𝑜𝑝𝑡 𝑧 . 𝑊𝑜𝑝𝑡 𝑧 = 𝑈 𝑥(𝑧) 𝑈 𝑦(𝑧) 𝐾𝑥𝑦 𝑧−∆ 𝑥𝑦 Room Impulse Response x Ref Mic. Err Mic. + Current Filter Parameters Updated Filter Parameters ⟶ 𝑊𝑜𝑝𝑡 𝑧 FxLMS Algorithm 41
  41. 41. AdaptiveANCinSmartRooms–ByImanArdekani ANC Analysis in Acoustic Domain Px(n) e(n) FxLMS S Control System W 42
  42. 42. AdaptiveANCinSmartRooms–ByImanArdekani Remote Active Noise Control y(n) Ly y-axis+ x-axis+ Lr 𝑊𝑜𝑝𝑡 e(n) Lo x(n) Lx Lx Ly Lo 𝑑 𝑦 𝑑 𝑥 𝜑 𝑦Lr 𝑑 𝑟 𝑑 𝑥𝑟 𝑑 𝑦𝑟 𝑊𝑜𝑝𝑡(𝑧) = 𝑊𝑜𝑝𝑡(𝑧)𝐾𝜌 𝑧−∆ 𝜌 Static gain Time-delay 43
  43. 43. AdaptiveANCinSmartRooms–ByImanArdekani Remote Active Noise Control 𝑊𝑜𝑝𝑡(𝑧) = 𝑊𝑜𝑝𝑡(𝑧)𝜌(𝑧) known unknown Px(n) e(n) FxLMS S Control System Remote ANC Algorithm W  44
  44. 44. AdaptiveANCinSmartRooms–ByImanArdekani Remote Active Noise Control Room Impulse Response x Ref Mic. Err Mic. + Current Filter Parameters Updated Filter Parameters ⟶ 𝑊𝑜𝑝𝑡 𝑧 FxLMS-based Remote ANC Algorithm +∆𝑒(𝑛) − 45
  45. 45. AdaptiveANCinSmartRooms–ByImanArdekani Remote Active Noise Control 𝑊𝑜𝑝𝑡(𝑧) = 𝑊𝑜𝑝𝑡(𝑧)𝜌(𝑧) known unknown Px(n) e(n) FxLMS S Control System Remote ANC Algorithm W  - Room Impulse Response ∆𝑒(𝑛) 46
  46. 46. EXPERIMENTS Setup Results 47
  47. 47. AdaptiveANCinSmartRooms–ByImanArdekani Experimental Setup Noise P S NI CRIO Smart Room Mic (Err Mic) Smart Room Mic (Ref Mic) Real-time Software (FPGA Design) 48
  48. 48. AdaptiveANCinSmartRooms–ByImanArdekani Experimental Results – Remote ANC 10 cm Initial noise level Final noise level Learning Room Acoustics 49
  49. 49. AdaptiveANCinSmartRooms–ByImanArdekani Experimental Results – Stability FxLMS 50 FwFxLMS
  50. 50. CONCLUSION Contributions Future Work 51
  51. 51. Contributions Investigated practical problems with adaptive ANC2 Moved towards the integration of ANC into Smart Rooms3 1 Improved the theoretical understanding of ANC Developed a flexible and high-performance experimental setup for ANC4 Published the results in high ranking journals5 Made international collaboration6 52
  52. 52. AdaptiveANCinSmartRooms–ByImanArdekani Future Work – ANC In Medical Devices MRI Incubator Hearing Aids 53 Other devices
  53. 53. AdaptiveANCinSmartRooms–ByImanArdekani Future Work – Smart Rooms Acoustic Sig Pro Medical Technology Room Book Intelligent Agents - Smart Hospital Rooms - Smart Assistive Rooms (for elderly and people with disabilities ) 54

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