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Real-Time Bayesian GSM Buzz Noise Removal

Real-Time Bayesian GSM Buzz Noise Removal. Han Lin and Simon Godsill {HL309|SJG30}@cam.ac.uk University of Cambridge Signal Processing Group. Outline. Introduction to GSM Buzz Noise Pulse and the Restoration Model Detection of Noise Pulses Removal of Noise Pulses Audio Demo and Results

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Real-Time Bayesian GSM Buzz Noise Removal

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  1. Real-Time Bayesian GSM Buzz Noise Removal Han Lin and Simon Godsill {HL309|SJG30}@cam.ac.uk University of Cambridge Signal Processing Group

  2. Outline • Introduction to GSM Buzz • Noise Pulse and the Restoration Model • Detection of Noise Pulses • Removal of Noise Pulses • Audio Demo and Results • Future Directions

  3. What is GSM Buzz? • Cellular phone (GSM ,TDMA, and CDMA) send out strong electromagnetic (EM) pulses during registration process • These pulses are received by audio amplifiers and line in circuits and causes noise known as GSM Buzz Buzz

  4. GSM Buzz (Interference Pulses) GSM Buzz Identification • Visual representation of GSM Buzz • Audio representation of GSM Buzz GSM Buzz can be everywhere

  5. signal processing approach Current Solutions to GSM Buzz • Reducing cell-phone transmission power • Changing transmission protocol • Equipping a telecoil (hearing aid) • Shielding All these solutions require hardware changes and are very difficult and expensive

  6. Practical Applications Statistical signal processing approach can provide last stage restoration for : • AV/ PA equipments • Recording studio • Desktop and car stereos • Portable players and recorders • Telephones • Hearing aids

  7. 217 Hz + harmonics Central Pulse (constant width clock) Decaying Tail (capacitance) Analysis of Noise Pulse

  8. The Restoration Model • x(n) - corrupted signal • g(n) - known interference template • b - constant scaling factor for amplitude difference • e(n) - white output noise • s(n) – original signal • m - location of the start of the noise pulse

  9. Design Strategy for GSM Buzz Removal • Assume Interference Template is known (or can be measured) • Assume central pulse has constant width • Detect Noise Pulse location - m’ • Estimate the scale factor - b • Remove Noise Pulse one by one

  10. Detection of Noise Pulses • Hardware Electromagnetic wave detector • Threshold detection/ slope detection • Cross correlation/ matched filter • Bayesian step detector • Autoregressive detector • The Bayesian template detector Detection is generally not a problem Detect

  11. The Bayesian Template Detector • x(n) - corrupted signal • g(n) - known interference template • b - constant scaling factor for amplitude difference • s(n) – original signal, assume to be autoregressive • m - location of the start of the noise pulse

  12. The Bayesian Template Detector • s(n) – original signal, assume to be autoregressive A contains AR coefficients a(i)

  13. The Bayesian Template Detector Assume Where k is large constant Define probability model for The Bayesian template detector : We wish to integrate out parameters b and σ1 in the detector to obtain an equation of only variable m

  14. The Bayesian Template Detector Solution for The Bayesian template detector :

  15. Bayesian Template Detector m’ Performance of Bayesian Template Detector Interfered Signal Plot P(m|x,g) MAX P(m|x,g)

  16. Removal of Noise Pulses with AR Template Interpolator Iterative model: • x(n) - corrupted signal LSAR interpolates the data in the central pulse region (assume data missing) • s(n) – original signal, assume to be autoregressive • g(n) - known interference template • b - constant scaling factor for amplitude difference • m’ - location of the start of the noise pulse

  17. Least Square AR Interpolator Iterative model: LSAR interpolates the data in the central pulse region (assume data missing) Assume x is autoregressive Solve for a(i) and the solution for LSAR is:

  18. iterate Dotted : corrupted Green: original Red :estimate b dip AR Template Interpolator r is estimated interference minimize e(n) to get b

  19. Central pulse Decaying tail Analysis of AR Template Interpolator Green : original Red : first estimate Black: second estimate

  20. Interfered Audio Interference Pattern Restored Audio “GSM Debuzz” Demo Original Audio

  21. Interfered Audio Restored Audio “GSM Debuzz” Demo (Pop and Speech) Original Audio Pop Speech

  22. GSM Debuzz Results No audible artifacts and improve SNR by 50dB www-sigproc.eng.cam.ac.uk/~hl309/DAFX2006/

  23. Real-time Consideration • For detection, use threshold detector or hardware EM detector • For restoration, use only one iteration • LSAR interpolation has computation complexity of O(L^2) using levinson-Durbin recursion • L is around 25 to 75 samples for CD quality audio

  24. Future Works Exponential decay model • Model the interference pulse as two exponential decays, estimate data in the central pulse region

  25. Scale Future Works Multi-channel Extension • Model the noise pulse of one channel as a scaled version of the other channel

  26. Thank You

  27. Real-Time Bayesian GSM Buzz Noise Removal Han Lin and Simon Godsill {HL309|SJG30}@cam.ac.uk University of Cambridge Signal Processing Group

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