How does AEC work?
| Site: | QSC |
| Course: | Q-SYS Level 1 Hybrid |
| Book: | How does AEC work? |
| Printed by: | Guest user |
| Date: | Sunday, 24 November 2024, 1:53 AM |
Description
Video Transcript
0:08
Welcome back. In order to understand how the AEC component works,
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let’s take a look at what happens to an audio signal that starts from the Far-End and is sent to the Near-End,
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and what AEC does to prevent its echoes from making it back to the Far-End.
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Here’s a diagram of this audio signal’s round trip journey.
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You’d never know it from the outside, but the Acoustic Echo Canceler puts the audio signal
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through a lot of sub-systems, including the Adaptive Filter and Adaptive Algorithm, Double-Talk Detection,
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Non-Linear Processing, Noise Reduction, and Comfort Noise. Let’s start with the Adaptive Filter.
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AEC’s goal is to eliminate any trace of the Far-End talker’s voice from the Near End microphone feed –
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including all direct and indirect paths from the loudspeaker to the microphone.
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In order to delete that noise, the AEC component needs to be able to predict what that noise will sound like.
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If we broadcast a sharp, impulsive sound over the loudspeaker such as a loud click or a gunshot, we could then
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record the signal arriving in the microphone, and obtain a recording that looks something like this.
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This first peak is the noise’s direct path from loudspeaker to microphone, and all the subsequent spikes
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represent the various reflections around the room – and the longer it takes to get to the microphone
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the more it has become attenuated during its travel. This image is known as a the room impulse response,
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and it's a predictive map of what happens to any noise that comes out of the loudspeaker.
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This room impulse response is used to create a Finite Impulse Response – or FIR – Filter,
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here in the Adaptive Filter part of the AEC system.
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When a signal comes from the Far-End, it is fed both to the Near End loudspeaker and to the Adaptive Filter.
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The FIR Filter is applied to the incoming signal to create its prediction of what that signal should
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sound like when it is received by the microphone.
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Then this noise is digitally subtracted from the Near End microphone signal – the result should be silence.
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The magic part is that the subtraction operation won’t affect any additional noise in the microphone signal,
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such as the Near End talker’s voice, letting the Far End talker have a crystal clear conversation
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without hearing his own echoes.
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No I can hear you loud and clear, so how’ve you guys been?
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However there is a fundamental problem with this model, which is the fact that the room impulse response is constantly changing.
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Whenever a door opens, or someone sits down, or if a butterfly flaps its wings, the surfaces in the Near End room have changed.
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Which means that the acoustic paths from the loudspeaker to the microphone have changed, so the room impulse response has changed.
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Now it’s not really a good idea to constantly broadcast big loud sounds to keep up with these changes.
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Instead the Adaptive Algorithm is used to constantly update the Filter, by monitoring the result of the subtraction operation
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and then adjusting the Filter until the result is as close to zero, or silence, as possible.
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This Adaptive Algorithm is always at work; trying to keep the filter converged with the dynamic room impulse response.
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However, it can only do its job when the Far End is talking and the Near End is silent.
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This is the only time when the microphone signal, after the subtraction operation, would equal zero.
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If the Far End is silent then there’s nothing to measure, and if the Near End is talking then there’s extra audio
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in the microphone so the result won’t be zero.
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This is a job for the Double-Talk-Detector, or DTD. The DTD listens to both the Far End and Near End microphones
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and determines if someone is speaking.
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If the Far End is speaking and the Near End is not, then it allows the Adaptive Algorithm to do its job of converging
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the Adaptive Filter to the room impulse response. In any other situation, the DTD will prevent the Adaptive Algorithm from working.
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Once all of these filters and algorithms have been applied to the signal, it still has several processes to go through before it
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makes it back to the Far End talker. First it goes through a Non-Linear Processor, or NLP.
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Because of the difficulty in completely converging the FIR filter with the room impulse response, there is bound
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to be some residual echo left in the microphone signal at this point.
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The Non-Linear Processor constants analyzes the audio at every instant, to determine if it is composed primarily of the
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near-end speech, or of residual far-end echoes. It pinpoints the areas that are made up of only echoes and attenuates those sections.
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The remaining echoes will be effectively inaudible over the desired near-end speech.
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Next in the processing path is Noise Reduction, or NR. Noise Reduction attempts to remove ambient noise room
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by listening for steady sustained noise in the signal and reducing it.
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This is so the Far End talker hears your voice, and not your air conditioning hum, the wind,
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or the lawn mowers outside the window, or the invading alien army.
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You can adjust the amount of Noise Reduction in your AEC’s control panel, and you can also enable or disable it with this button.
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Finally, the Comfort Noise block is a special feature of the Q-SYS AEC system. After going through Non-Linear Processing
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and Noise-Reduction, the Far End should hear the Near End talker loud and clear with everything else being quiet.
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Too quiet. If the Near End talker stops speaking, the line might go silent and give the impression that the
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telephone line has been disconnected.
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Basically it’s a byproduct of the AEC doing its job too well. It actually sounds very strange...
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When there’s complete silence...In between voices, right?
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So the Comfort Noise can be added, which is an artificial low-pass noise signal that makes it sound like there’s
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still a connection when nobody is talking.
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You can adjust the level of the Comfort Noise added in the control panel as well. The only other features in the
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control panel are a master bypass to turn off your AEC, and the Echo Return Loss Enhancement meter, which
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shows you how much, in decibels, the Far-End’s echoes have been attenuated in the return signal.
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The nominal level for this meter will vary depending on the distances between your loudspeakers and
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your microphones, but it should still give you a good idea of how effectively your AEC is operating.
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So that’s what happens inside the magic box – which fortunately you’ll never need to worry about.
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All you have to do is make sure it’s connected properly and then forget about it. Unlike a lot of products out there,
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the Q-SYS echo cancelation is automatically included as part of the Designer software – there is no additional hardware to set-up
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and no additional fees. It’s simply part of the Q-SYS package. Now in the next section we’ll look at how to set it up in conjunction
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with the Softphone component to create a teleconferencing system so feel free to move on whenever you’re ready.
Lesson Description
Explore the many processes that AEC uses to silence the echoes of the Far-End caller’s voice.
Tips and Definitions
Room Impulse Response: A diagram of what happens to a sharp, impulsive noise after traveling through the room.
Adaptive Filter: This process uses the Finite Impulse Reponse (FIR) Filter created from the Room Impulse Response by applying it to the Far-End to predict what it will sound like after traveling through the room
Adaptive Algorithm: This process analyzes the result of the subtraction operation and adjusts the Adaptive Filter accordingly.
Double-Talk Detector (DTD): This detector only lets the Adaptive Algorithm do its job when the Far-End is speaking and the Near-End is not.
Non-Linear Processor (NLP): This process analyzes the audio for any remaining residual Far End echoes and attenuates the appropriate sections.
Noise Reduction (NR): This process eliminates steady background noises in the Near-End.
Comfort Noise (CN): This process reintroduces soft white noise to prevent the line from sounding like a disconnection.