real time bayesian gsm buzz noise removal l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Real-Time Bayesian GSM Buzz Noise Removal PowerPoint Presentation
Download Presentation
Real-Time Bayesian GSM Buzz Noise Removal

Loading in 2 Seconds...

play fullscreen
1 / 27

Real-Time Bayesian GSM Buzz Noise Removal - PowerPoint PPT Presentation


  • 130 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Real-Time Bayesian GSM Buzz Noise Removal' - lyn


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
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
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 identification

GSM Buzz (Interference Pulses)

GSM Buzz Identification
  • Visual representation of GSM Buzz
  • Audio representation of GSM Buzz

GSM Buzz can be everywhere

current solutions to gsm buzz

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

practical applications
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
analysis of noise pulse

217 Hz + harmonics

Central Pulse (constant width clock)

Decaying Tail (capacitance)

Analysis of Noise Pulse
the restoration model
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
design strategy for gsm buzz removal
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
detection of noise pulses
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

the bayesian template detector
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
the bayesian template detector12
The Bayesian Template Detector
  • s(n) – original signal, assume to be autoregressive

A contains AR coefficients a(i)

the bayesian template detector13
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

the bayesian template detector14
The Bayesian Template Detector

Solution for The Bayesian template detector :

performance of bayesian template detector

Bayesian Template Detector

m’

Performance of Bayesian Template Detector

Interfered Signal

Plot P(m|x,g)

MAX P(m|x,g)

removal of noise pulses with ar template interpolator
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
least square ar interpolator
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:

ar template interpolator

iterate

Dotted : corrupted

Green: original

Red :estimate

b

dip

AR Template Interpolator

r is estimated interference

minimize e(n) to get b

analysis of ar template interpolator

Central pulse

Decaying tail

Analysis of AR Template Interpolator

Green : original

Red : first estimate

Black: second estimate

gsm debuzz demo

Interfered Audio

Interference Pattern

Restored Audio

“GSM Debuzz” Demo

Original Audio

gsm debuzz demo pop and speech

Interfered Audio

Restored Audio

“GSM Debuzz” Demo (Pop and Speech)

Original Audio

Pop

Speech

gsm debuzz results
GSM Debuzz Results

No audible artifacts and improve SNR by 50dB

www-sigproc.eng.cam.ac.uk/~hl309/DAFX2006/

real time consideration
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
future works exponential decay model
Future Works Exponential decay model
  • Model the interference pulse as two exponential decays, estimate data in the central pulse region
future works multi channel extension

Scale

Future Works Multi-channel Extension
  • Model the noise pulse of one channel as a scaled version of the other channel
real time bayesian gsm buzz noise removal27

Real-Time Bayesian GSM Buzz Noise Removal

Han Lin and Simon Godsill

{HL309|SJG30}@cam.ac.uk

University of Cambridge

Signal Processing Group