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A frequently occurring distortion in speech telecommunication scenarios is echoes, which have either an electric or acoustic cause. Electric echoes appear in analogue networks at points of impedance mismatch. Since a majority of telecommunication networks today are digital, such electric echoes are mostly of historical interest and not discussed here further. Acoustic echoes however is a problem which has become more  important, especially with the increasing use of teleconferencing services. Such acoustic echoes appear when the speech of a person A is played through the loudspeakers for a person B, such that the loudspeaker sound is picked up by the microphone of person B and transmitted back to person A. If the delay would be mere milliseconds, then person A would perceive the feedback signal as reverberation, which is not too disturbing (in some circumstances such an effect, known as side-talk, could actually be a useful feature indicating that the microphone is recording). However, typically the transmission delay is above 100ms, such that the feedback is perceived as an echo, which is usually highly disconcerting and disturbing. Even worse, sometimes the acoustic echo signal completes the feedback loop and goes in a circle, again and again. If the feedback signal is attenuated on each loop, then the feedback slowly diminishes, but sometimes the feedback loop can amplify the signal such that the signal quickly escalates up to the physical limit of hardware or until the loudspeakers blow up. It is thus clear that echoes must be avoided in real-life systems.

Fortunately, echo cancellation is well-understood problem with "standard" solutions available. In short, these methods are based on estimating the acoustic room-impulse-response (RIR), filtering the loudspeaker signal with the RIR to obtain an estimate of the acoustic echo, and subtracting the estimated echo from the microphone signal. Though it is a classic problem with off-the-shelf solutions available, it remains a difficult problem. Central difficulties include:

  • The RIR is not stationary and must therefore be estimated on-line as an adaptive filter. The RIR changes whenever a door or window is opened or closed, furniture are moved or people move within the room, and even when the temperature of the room changes. Changes are even more rapid when using mobile, handheld or wearable devices, when the location of the device can change rapidly. In the worst case, the microphone and loudspeaker can be on different devices such that their acoustic distance can change rapidly.
  • When the RIR has a long tail, that is, when the room has a long reverberation, then the corresponding filters must be long, which increases computational complexity. Fast adaptation to changing RIRs also increases computational requirements.

Observe that echo cancellation methods refer to subtracting the estimated echo from the microphone signal. In contrast, noise attenuation methods multiply the microphone signal with a positive scalar, such that the output signal approximates the echo-free signal. The essential difference is that multiplicative methods generally estimate only the energy/magnitude of the signal, while subtractive methods try to match both magnitude and phase. The benefit of subtractive methods is that the output quality is generally better since the removal-method matches the physical process which creates the signal. Multiplicative methods however are much more robust than subtractive methods; in particular, if the phase is incorrectly estimated, then a subtractive method can increase the amount of noise in a signal. In the worst case, an echo canceller poorly matched to the room response can generate a catastrophic feedback loop, whereas multiplicative methods can be designed to never increase the amount of noise.

Echo suppression methods use this insight to remove acoustic echo with a multiplicative method similar to that of spectral subtraction or Wiener filtering used in noise attenuation.

The acoustic feedback loop in telecommunication applications.

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