1. Real-Time Bayesian GSM Buzz Noise Removal
Han Lin and Simon Godsill
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 Identification
Visual representation of GSM Buzz
Audio representation of GSM Buzz
GSM Buzz can be everywhere
GSM Buzz (Interference Pulses)

5. 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
signal processing approach

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
Can restore Restore multi-channel digital recordings (when channels are coded separately), say 5.1 channel DVD
Can Restore multi-channel digital audio transmissions (independent channels), for example, multi-channel internet streaming audio, mp3, real audio, wma.
Can Restore wireless audio communications (using FM radio to restore Digital Radio DAB)

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

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

16. Removal of Noise Pulses with AR Template Interpolator
Iterative model:
LSAR interpolates the data in the central pulse region (assume data missing)
s(n) – original signal, assume to be autoregressive
x(n) - corrupted signal
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. AR Template Interpolator
iterate
r is estimated interference
minimize e(n) to get b
Dotted : corrupted
Green : original
Red : estimate b
We can extend the single channel autoregressive model to 2 channel autoregressive model,
where we add a new summation with time-shifted data from channel 2.
The first summation, the CO_CHANNEL term, is simply the familiar single channel AR model,
and the second summation (the CROSS_CHANNEL term) contains data of channel 2.
dip

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

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

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

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

26. Thank You

27. Real-Time Bayesian GSM Buzz Noise Removal
Han Lin and Simon Godsill
University of Cambridge
Signal Processing Group