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Real-Time Bayesian GSM Buzz Noise Removal Han Lin and Simon Godsill {HL309|SJG30}@cam.ac.uk...
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Transcript of Real-Time Bayesian GSM Buzz Noise Removal Han Lin and Simon Godsill {HL309|SJG30}@cam.ac.uk...
Real-Time Bayesian Real-Time Bayesian GSM Buzz Noise RemovalGSM Buzz Noise Removal
Han Lin and Simon Godsill
{HL309|SJG30}@cam.ac.uk
University of Cambridge
Signal Processing Group
OutlineOutline
Introduction to GSM BuzzNoise Pulse and the Restoration ModelDetection of Noise PulsesRemoval of Noise Pulses Audio Demo and ResultsFuture Directions
What is GSM Buzz?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
GSM Buzz IdentificationGSM Buzz Identification
Visual representation of GSM Buzz
GSM Buzz (Interference Pulses)
Audio representation of GSM Buzz
GSM Buzz can be everywhere
Current Solutions to GSM BuzzCurrent Solutions to GSM Buzz
Reducing cell-phone transmission powerChanging transmission protocolEquipping a telecoil (hearing aid)Shielding
All these solutions require hardware changes and are very difficult and expensive
signal processing approach
Practical Practical ApplicationsApplications
AV/ PA equipmentsRecording studioDesktop and car stereosPortable players and recordersTelephonesHearing aids
Statistical signal processing approach can provide last stage restoration for :
Analysis of Noise PulseAnalysis of Noise Pulse
Central Pulse (constant width clock)
Decaying Tail (capacitance)
217 Hz + harmonics
The Restoration ModelThe 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
Design Strategy for Design Strategy for GSM Buzz RemovalGSM Buzz Removal
Assume Interference Template is known (or can be measured)
Assume central pulse has constant widthDetect Noise Pulse location - m’Estimate the scale factor - bRemove Noise Pulse one by one
Detection of Noise PulsesDetection of Noise Pulses Hardware Electromagnetic wave detector Threshold detection/ slope detection Cross correlation/ matched filter Bayesian step detector Autoregressive detector The Bayesian template detector
Detect
Detection is generally not a problem
The Bayesian Template DetectorThe Bayesian Template Detector
x(n) - corrupted signal g(n) - known interference template
s(n) – original signal, assume to be autoregressive
b - constant scaling factor for amplitude difference
m - location of the start of the noise pulse
The Bayesian Template DetectorThe Bayesian Template Detector s(n) – original signal, assume to be autoregressive
A contains AR coefficients a(i)
The Bayesian Template DetectorThe Bayesian Template Detector
Assume
Where k is large constant
We wish to integrate out parameters b and σ1 in the detector to obtain an equation of only variable m
Define probability model for The Bayesian template detector :
The Bayesian Template DetectorThe Bayesian Template Detector
Solution for The Bayesian template detector :
Performance of Bayesian Performance of Bayesian Template DetectorTemplate Detector
Interfered Signal
Bayesian Template Detector
Plot P(m|x,g)
MAX P(m|x,g)m’
Removal of Noise Pulses with Removal of Noise Pulses with AR AR Template InterpolatorTemplate Interpolator
LSAR interpolates the data in the central pulse region (assume data missing)
Iterative model:
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
Least Square AR InterpolatorLeast Square AR Interpolator
LSAR interpolates the data in the central pulse region (assume data missing)
Iterative model:
Assume x is autoregressive
Solve for a(i) and the solution for LSAR is:
AR Template InterpolatorAR Template Interpolator
iterate r is estimated interference
minimize e(n) to get b
b
Dotted : corruptedGreen : originalRed : estimate
dip
Analysis of AR Template Analysis of AR Template InterpolatorInterpolator
Central pulse
Decaying tail
Green : original
Red : first estimate
Black: second estimate
““GSM Debuzz” DemoGSM Debuzz” Demo
Interference Pattern
Original Audio
Interfered Audio
Restored Audio
““GSM Debuzz” DemoGSM Debuzz” Demo ( (Pop and Speech)Pop and Speech)
Original Audio
Interfered Audio
Restored Audio
PopSpeech
GSM Debuzz ResultsGSM Debuzz Results
No audible artifacts and improve SNR by 50dB
www-sigproc.eng.cam.ac.uk/~hl309/DAFX2006/
Real-time ConsiderationReal-time ConsiderationFor detection, use threshold detector or
hardware EM detector For restoration, use only one iterationLSAR interpolation has computation
complexity of O(L^2) using levinson-Durbin recursion
L is around 25 to 75 samples for CD quality audio
Future Works Future Works Exponential decay modelExponential decay model
Model the interference pulse as two exponential decays, estimate data in the central pulse region
Future Works Future Works Multi-channel Extension Multi-channel Extension
Model the noise pulse of one channel as a scaled version of the other channel
Scale
Thank YouThank You
Real-Time Bayesian Real-Time Bayesian GSM Buzz Noise RemovalGSM Buzz Noise Removal
Han Lin and Simon Godsill
{HL309|SJG30}@cam.ac.uk
University of Cambridge
Signal Processing Group