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 Future Directions

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 Visual representation of GSM Buzz Audio representation of GSM Buzz GSM Buzz can be everywhere GSM Buzz (Interference Pulses)

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

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)

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

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 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 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 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 Detector s(n) – original signal, assume to be autoregressive A contains AR coefficients a(i)

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 Detector Solution for The Bayesian template detector :

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

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

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 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

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

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

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

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

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 Model the interference pulse as two exponential decays, estimate data in the central pulse region

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

Thank You

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