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06-07-2010, University of Oldenburg, MEDI-AKU-SIGNAL Kolloquium

Signal Processing Algorithms for Wireless Acoustic Sensor Networks Alexander Bertrand Electrical Engineering Department (ESAT) Katholieke Universiteit Leuven. 06-07-2010, University of Oldenburg, MEDI-AKU-SIGNAL Kolloquium. Outline. Introduction Multi-channel Wiener filter (MWF)

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06-07-2010, University of Oldenburg, MEDI-AKU-SIGNAL Kolloquium

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  1. Signal Processing Algorithms for Wireless Acoustic Sensor NetworksAlexander BertrandElectrical Engineering Department (ESAT)Katholieke Universiteit Leuven 06-07-2010, University of Oldenburg, MEDI-AKU-SIGNAL Kolloquium

  2. Outline Introduction Multi-channel Wiener filter (MWF) Example: distributed MWF in binaural hearing aids DANSE in fully connected WASN Tree-DANSE Multi-speaker VAD Noise reduction Tracking of speech power

  3. Outline • Introduction • Multi-channel Wiener filter (MWF) • Example: distributed MWF in binaural hearing aids • DANSE in fully connected WASN • Tree-DANSE • Multi-speaker VAD

  4. Traditional sensor array DSP  known / fixed sensor positions  Sharp angle  centralized processing  #microphones is limited  Long distance(SNR drops 6dB for each doubling of distance) Sensor array DSP 4

  5. Distributed sensor arrays Wireless acoustic sensor network (WASN) • More spatial information • More sensors • Subset: high SNR recordings 5

  6. Challenges Distributed sensor arrays 4) Subset selection 3) Distributed processing 1) Unknown/changing positions, link failure  ADAPTIVE 2) Bandwidth efficiency 6

  7. Outline • Introduction • Multi-channel Wiener filter (MWF) • Example: distributed MWF in binaural hearing aids • DANSE in fully connected WASN • Tree-DANSE • Multi-speaker VAD

  8. Multi-channel Wiener Filtering (MWF) • Goal: estimate speech component in 1 of the N microphones • Output = sum of filtered microphone signals: W1 + W2 Clean speech W3 W4

  9. Multi-channel Wiener Filtering (MWF) • Goal: estimate speech component in 1 of the N microphones • Output = sum of filtered microphone signals: W1 + W2 Clean speech W3 W4

  10. Multi-channel Wiener Filtering (MWF) • Goal: estimate speech component in 1 of the N microphones • Output = sum of filtered microphone signals: • Needs: - N x N noise+speech correlation matrix Ryy - N x 1 clean speech correlation (column of Rdd) • Rddcan be estimated using Rdd= Ryy- Rnn using voice activity detection (VAD)mechanism W1 + W2 Clean speech W3 W4

  11. Multi-channel Wiener Filtering (MWF) • RECAP • Given: N microphone signals • Choose one (arbitrary) reference microphone • MWF computes optimal filters such that sum of outputs is as close as possible to speech component in target microphone

  12. Noise frame: destructive interference  Noise = electro music F1 + F2 F3 F4

  13. Speech frame: constructive interference  Noise = electro music F1 + F2 F3 F4

  14. Outline • Introduction • Multi-channel Wiener filter (MWF) • Example: distributed MWF in binaural hearing aids • DANSE in fully connected WASN • Tree-DANSE • Multi-speaker VAD • Subset selection • Conclusions

  15. Example: binaural hearing aids large bandwidth needed full matrix inversion = 2-node WASN Binaural link MWF left MWF right 15

  16. Example: binaural hearing aids + + Converges to optimum if single desired source (Doclo et al., 2007) Binaural link w11 g12 g21 w22 16

  17. Motivation for DANSE • > 2 nodes ?e.g. supporting external sensor nodes or multiple hearing aid users. 17

  18. Motivation for DANSE • > 2 nodes ?e.g. supporting external sensor nodes or multiple hearing aid users. 18

  19. Motivation for DANSE • > 2 nodes ?e.g. supporting external sensor nodes or multiple hearing aid users. 19

  20. Motivation for DANSE • > 2 nodes ?e.g. supporting external sensor nodes or multiple hearing aid users. 20

  21. Motivation for DANSE • > 2 nodes • Multiple desired sourcese.g. conversation monitoring. 21

  22. Motivation for DANSE • > 2 nodes • Multiple desired sourcese.g. conversation monitoring. 22

  23. Outline • Introduction • Multi-channel Wiener filter (MWF) • Example: distributed MWF in binaural hearing aids • DANSE in fully connected WASN • Tree-DANSE • Multi-speaker VAD

  24. DANSE • Previous requires more general framework:Distributed adaptive node-specific signal estimation (DANSE) • Allows for multiple nodes (fully connected topology) • Allows for multiple target sources: Estimating K sources requires communication of K-channel signals(DANSEK) 24

  25. DANSE • Considered here: • Fully connected WSN • Multi-channel sensor signal observations • Goal: each node estimates node-specific signal, but common latent signal subspace (dimension= # targets)

  26. 3 nodes, fully connected 26

  27. Binaural hearing aids (revisited) + + Binaural link w11 g12 g21 w22 27

  28. Binaural hearing aids (revisited) + + Converges to optimum if #desired sources ≤ 2 auxiliary channels(capture signal space) J=2, DANSE2 (K=2) Binaural link w11(2) g12(2) g21(2) w22(2) w11(1) g12(1) g21(1) w22(1) 28

  29. Binaural hearing aids (revisited) + + Converges to optimum if K= # desired sources J=2, DANSEK Binaural link 29

  30. Sequential updating Sequential round-robin update

  31. DANSE with simultaneous updating • Simultaneous updating: parallel computing • Sometimes convergence to optimal solution, but not always • Solution:relaxationyields convergence and optimality: 31

  32. DANSE with simultaneous updating Without relaxation (S-DANSE) 4 nodes, 3-6 sensors/node 32

  33. DANSE with simultaneous updating With relaxation (rS-DANSE) 4 nodes, 3-6 sensors/node 33

  34. DANSE audio demo (tracking omitted) Unfiltered Centralized MWF rS-DANSE 34

  35. Robust DANSE • Theory: DANSE == centralized MWF, but… 35

  36. Robust DANSE • Numerical errors due to: • Estimation errors in Rdd (especially at low SNR nodes)  ripple effect • Reference microphones are close to each other ill-conditioned basis for signal subspace • Solution: estimate speech component in communicated signals, preferably from high SNR nodes (= Robust DANSE or R-DANSE) • Convergence is proven under certain dependency conditions 36

  37. Outline • Introduction • Multi-channel Wiener filter (MWF) • Example: distributed MWF in binaural hearing aids • DANSE in fully connected WASN • Tree-DANSE • Multi-speaker VAD

  38. What if not fully connected?

  39. What if not fully connected? Nodes must pass on information from other nodes 1) Nodes act as relays(virtually fully connected): - huge increase in bandwidth if limited connections - routing problem 2) Nodes broadcast the sum of all filtered inputs: - no increase in bandwidth - no routing problem (?)

  40. What if not fully connected? 40

  41. What if not fully connected? FEEDBACK !!

  42. What if not fully connected? • Intuition • Theoretical analysis • Conclusion: feedback causes major problems • Direct feedback (one edge) vs. indirect feedback (loops)

  43. Direct feedback cancellation • Transmitter feedback cancellation

  44. Direct feedback cancellation • Receiver feedback cancellation

  45. What if not fully connected? • Intuition • Theoretical analysis • Conclusion: feedback causes major problems • Direct feedback (one edge) vs. indirect feedback (loops) • Prune to tree topology  T-DANSE (= still optimal output!!)

  46. Outline • Introduction • Multi-channel Wiener filter (MWF) • Example: distributed MWF in binaural hearing aids • DANSE in fully connected WASN • Tree-DANSE • Multi-speaker VAD

  47. Multi-speaker VAD speaker microphone - Goal: Track individual speech power of multiple simultaneous speakers or other non-stationary sources (VAD) - Exploit spatial diversity from WASN 47

  48. Multi-speaker VAD WASN’s ! • Ad-hoc microphone array • Assumptions: • Speakers in near-field • Speakers are independent • Limited noise/reverberance • Sources to track are well-grounded (= they attain zero-values) • Advantages: • Array geometry unknown • Speaker positions unknown • Energy-based low data rate  synchronization not crucial 48

  49. Data model

  50. Data model

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