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