  Acoustic Echo Cancellation (AEC) plays a key role in voice interaction. Due
to the explicit mathematical principle and intelligent nature to accommodate
conditions, adaptive filters with different types of implementations are always
used for AEC, giving considerable performance. However, there would be some
kinds of residual echo in the results, including linear residue introduced by
mismatching between estimation and the reality and non-linear residue mostly
caused by non-linear components on the audio devices. The linear residue can be
reduced with elaborate structure and methods, leaving the non-linear residue
intractable for suppression. Though, some non-linear processing methods have
already be raised, they are complicated and inefficient for suppression, and
would bring damage to the speech audio. In this paper, a fusion scheme by
combining adaptive filter and neural network is proposed for AEC. The echo
could be reduced in a large scale by adaptive filtering, resulting in little
residual echo. Though it is much smaller than speech audio, it could also be
perceived by human ear and would make communication annoy. The neural network
is elaborately designed and trained for suppressing such residual echo.
Experiments compared with prevailing methods are conducted, validating the
effectiveness and superiority of the proposed combination scheme.
