2. Introduction
Teleconferencing systems are expected to provide a high sound
quality. Speech by the far end speaker is captured by the near end
microphone and being sent back to him as echo. Acoustic echoes
cause great discomfort to the users since their own speech (delayed
version) is heard during conversation. The echo has been a big issue
in communication networks. Hence this presentation is devoted to
the investigation and development of an effective way to control the
acoustic echo in hands-free communications.
4. Acoustic Echo
Sound is created by the loudspeaker and after Reflection return to the
microphone and undesirable echo is heard during a conversation .
Examples:
– Hands-free car phone systems.
– A standard telephone or cell phone in speakerphone.
–Physical coupling (vibrations of the loudspeaker transfer to the microphone
via the handset casing).
5. Seriousness of AE compared to network
echo in telephony
Long delay
The echo path may change according movement of microphone
• The background noise can be strong and non-stationary
6. ACOUSTIC ECHO PROBLEM’S
SOLUTIONS
Using some physical tools to remove noise.
Another solution is to Develop an algorithm for removing the
Acoustic echo so that transmission to the far-end is echo free.
This is done by the Acoustic echo canceller.
8. Working of Acoustic Echo
Canceller
Far end Signal travels out the loudspeaker, bounces around in the
room, and convolved with room impulse response to produce far end
echo .This far end echo is picked up by the microphone.
The adaptive filter takes far end signal ,generates an echo replica and
subtracts it from far end echo to generate an error signal .This error
signal is transmitted back to the far-end speaker.
9. General AEC procedure
A far-end signal is delivered to acoustic feedback synthesizer.
The far-end signal is reproduced by the speaker.
A microphone picks up the sound.
The far-end signal is filtered and delayed.
The filtered far-end signal is subtracted from the near-end signal.
The resultant signal should not contain any direct or reverberated sound produced by
the speaker.
10. Requirements
Fast convergence of adaptive filter.
Stable convergence
No performance degradation for the real speech signal
11. LMS Algorithm
Most popular adaptation algorithm is LMS
– Define cost function as mean-squared error
Simple, no matrices calculation involved in the adaptation
Based on the method of steepest descent
Move towards the minimum on the error surface to get to minimum gradient of the
error surface estimated at every iteration
